Method, apparatus, and system for fall-down detection based on a wireless signal

ABSTRACT

Methods, apparatus and systems for periodic or transient motion detection, e.g. fall event detection, based on wireless signals are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue; a receiver configured for receiving a second wireless signal through the wireless multipath channel; and a processor. The second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a target motion of an object in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, computing a time series of spatial-temporal information (STI) of the object based on the TSCI, and detecting the target motion of the object based on the time series of STI (TSSTI).

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. patent application withdocket number OWI-0052US4, entitled “METHOD, APPARATUS, AND SYSTEM FORWIRELESS MATERIAL SENSING,” filed on Feb. 20, 2021, related to U.S.patent application with docket number OWI-0052US5, entitled “METHOD,APPARATUS, AND SYSTEM FOR WIRELESS WRITING TRACKING,” filed on Feb. 20,2021, and related to U.S. patent application with docket numberOWI-0037US1, entitled “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS MOTIONRECOGNITION,” filed on Feb. 20, 2021, each of which is expresslyincorporated by reference herein in its entirety.

The present application hereby incorporates by reference the entirety ofthe disclosures of, and claims priority to, each of the following cases:

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TECHNICAL FIELD

The present teaching generally relates to wireless motion detection.More specifically, the present teaching relates to periodic or transientmotion detection, e.g. fall event detection, based on wireless signalsby processing wireless channel information (CI).

BACKGROUND

As the population ages worldwide, our society should take more and morearduous responsibilities to provide medical care for the elderly people.Among all of the accidents to the elderly people, fall events representthe most frequent ones. A report from the World Health Organization(WHO) indicates that 20%-30% of older people who fall suffer moderate tosevere injuries. Falls can even cause death. In all regions of theworld, death rates caused by falls are the highest among adults over theage of 60.

Moreover, the damage caused by the falls is not only reflected in theimmediate injury of the body, but also in all subsequent adverse effectscaused by the lack of timely assistance, especially for those who livealone. Therefore, a real-time indoor fall detection system with timelyand automatic alarm is highly desirable, and could potentially savelives by requesting external help timely.

The great importance of fall detection has driven the development ofvarious systems, which can be roughly divided into two categories:wearable and non-contact systems. The wearable techniques require usersto wear special devices, including electrocardiogram (ECG) sensors,pressure sensors, accelerometer, gyroscope, smart watches, andsmartphones, etc., to track the motion of their bodies. However, inaddition to the potential false alarms of wearable systems, it iscumbersome and sometimes impractical to ask users especially the elderpeople to carry specific sensors. This inspires the development ofnon-contact systems. The most common non-contact systems arevision-based. In an existing non-contact system, an array of cameras,infrared sensors, or depth cameras, need to be deployed to monitor anarea of interest. As such, vision-based systems are limited by theirvisibility requirement and may also bring privacy concerns, especiallyin some specific environments such as bathrooms and bedrooms.

Inspired by the fact that the radio frequency (RF) signals can bealtered by the propagation environment, the concept of wireless sensingpresents the opportunities of sensing human activities passively andmany wireless technologies, such as Doppler radar and WiFi signals havebeen explored to detect falls. However, the existing RF-based approachesrequire either specialized devices or re-training in new environment,which is impractical in commercial indoor fall detection systems.

SUMMARY

The present teaching generally relates to wireless motion detection.More specifically, the present teaching relates to periodic or transientmotion detection, e.g. fall event detection, based on wireless signalsby processing wireless channel information (CI).

In one embodiment, a system for target motion detection is described.The system comprises: a transmitter configured for transmitting a firstwireless signal through a wireless multipath channel of a venue; areceiver configured for receiving a second wireless signal through thewireless multipath channel; and a processor. The second wireless signaldiffers from the first wireless signal due to the wireless multipathchannel that is impacted by a target motion of an object in the venue.The processor is configured for: obtaining a time series of channelinformation (TSCI) of the wireless multipath channel based on the secondwireless signal, computing a time series of spatial-temporal information(STI) of the object based on the TSCI, and detecting the target motionof the object based on the time series of STI (TSSTI).

In another embodiment, a wireless device of a wireless target motiondetection system is described. The wireless device comprises: aprocessor; a memory communicatively coupled to the processor; and areceiver communicatively coupled to the processor. An additionalwireless device of the wireless target motion detection system isconfigured for transmitting a first wireless signal through a wirelessmultipath channel of a venue to the receiver. The receiver is configuredfor receiving a second wireless signal through the wireless multipathchannel. The second wireless signal differs from the first wirelesssignal due to the wireless multipath channel that is impacted by atarget motion of an object in the venue. The processor is configuredfor: obtaining a time series of channel information (TSCI) of thewireless multipath channel based on the second wireless signal,computing a time series of spatial-temporal information (TSSTI) of theobject based on the TSCI, and detecting the target motion of the objectbased on at least one of: the TSSTI or the TSCI.

In yet another embodiment, a method of a target motion detection systemis described. The method comprises: transmitting, by a transmitter, afirst wireless signal through a wireless multipath channel of a venue toa receiver; receiving, by the receiver in an online stage of the targetmotion detection system, a second wireless signal through the wirelessmultipath channel, wherein the second wireless signal differs from thefirst wireless signal due to the wireless multipath channel that isimpacted by a target motion of an object in the venue; obtaining a timeseries of channel information (TSCI) of the wireless multipath channelbased on the second wireless signal, using a processor, a memorycommunicatively coupled with the processor and a set of instructionsstored in the memory; computing a time series of spatial-temporalinformation (TSSTI) of the object based on the TSCI; and detecting thetarget motion of the object based on at least one of: the TSSTI or theTSCI.

Other concepts relate to software for implementing the present teachingon detecting and monitoring fall-down event and other transient orperiodic motions of an object. Additional novel features will be setforth in part in the description which follows, and in part will becomeapparent to those skilled in the art upon examination of the followingand the accompanying drawings or may be learned by production oroperation of the examples. The novel features of the present teachingsmay be realized and attained by practice or use of various aspects ofthe methodologies, instrumentalities and combinations set forth in thedetailed examples discussed below.

BRIEF DESCRIPTION OF DRAWINGS

The methods, systems, and/or devices described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings.

FIG. 1 illustrates an exemplary wireless indoor rich-scatteringenvironment, according to some embodiments of the present disclosure.

FIG. 2 illustrates a flow chart of an exemplary method for wirelesslydetecting a target motion of an object, according to some embodiments ofthe present teaching.

FIG. 3 illustrates an exemplary block diagram of a first wireless deviceof a wireless motion detection system, according to one embodiment ofthe present teaching.

FIG. 4 illustrates an exemplary block diagram of a second wirelessdevice of a wireless motion detection system, according to oneembodiment of the present teaching.

FIGS. 5A-5D illustrate an exemplary spatial auto-correlation function(ACF) and its differentials for electromagnetic (EM) wave components,according to one embodiment of the present teaching.

FIG. 6 illustrates an exemplary flowchart of a wireless fall detectionsystem, according to one embodiment of the present teaching.

FIGS. 7A-7B illustrate an exemplary application of the principle ofsegmental locally normalized dynamic time warping (SLN-DTW) in seriessanitization, according to one embodiment of the present teaching.

FIG. 8 illustrates an exemplary experiment setup for line-of-sight (LOS)and non-LOS scenarios, according to one embodiment of the presentteaching.

FIGS. 9A-9D illustrate exemplary instances of speed and accelerationpatterns for “walking-then-fall” and “sitting-down” scenarios, accordingto one embodiment of the present teaching.

FIGS. 10A-10C illustrate exemplary templates of speed and accelerationin one-dimensional and two-dimensional spaces, according to oneembodiment of the present teaching.

FIG. 11 illustrates an exemplary performance comparison between adynamic time warping (DTW) method and a threshold method, according toone embodiment of the present teaching.

DETAILED DESCRIPTION

In one embodiment, the present teaching discloses a method, apparatus,device, system, and/or software(method/apparatus/device/system/software) of a wireless monitoringsystem. A time series of channel information (CI) of a wirelessmultipath channel (channel) may be obtained (e.g. dynamically) using aprocessor, a memory communicatively coupled with the processor and a setof instructions stored in the memory. The time series of CI (TSCI) maybe extracted from a wireless signal (signal) transmitted between a Type1 heterogeneous wireless device (e.g. wireless transmitter, TX) and aType 2 heterogeneous wireless device (e.g. wireless receiver, RX) in avenue through the channel. The channel may be impacted by an expression(e.g. motion, movement, expression, and/or change inposition/pose/shape/expression) of an object in the venue. Acharacteristics and/or a spatial-temporal information (STI, e.g. motioninformation) of the object and/or of the motion of the object may bemonitored based on the TSCI. A task may be performed based on thecharacteristics and/or STI. A presentation associated with the task maybe generated in a user-interface (UI) on a device of a user. The TSCImay be a wireless signal stream. The TSCI or each CI may bepreprocessed. A device may be a station (STA). The symbol “A/B” means “Aand/or B” in the present teaching.

The expression may comprise placement, placement of moveable parts,location, position, orientation, identifiable place, region, spatialcoordinate, presentation, state, static expression, size, length, width,height, angle, scale, shape, curve, surface, area, volume, pose,posture, manifestation, body language, dynamic expression, motion,motion sequence, gesture, extension, contraction, distortion,deformation, body expression (e.g. head, face, eye, mouth, tongue, hair,voice, neck, limbs, arm, hand, leg, foot, muscle, moveable parts),surface expression (e.g. shape, texture, material, color,electromagnetic (EM) characteristics, visual pattern, wetness,reflectance, translucency, flexibility), material property (e.g. livingtissue, hair, fabric, metal, wood, leather, plastic, artificialmaterial, solid, liquid, gas, temperature), movement, activity,behavior, change of expression, and/or some combination.

The wireless signal may comprise: transmitted/received signal, EMradiation, RF signal/transmission, signal in licensed/unlicensed/ISMband, bandlimited signal, baseband signal, wireless/mobile/cellularcommunication signal, wireless/mobile/cellular network signal, meshsignal, light signal/communication, downlink/uplink signal,unicast/multicast/broadcast signal, standard (e.g. WLAN, WWAN, WPAN,WBAN, international, national, industry, defacto, IEEE, IEEE 802,802.11/15/16, WiFi, 802.11n/ac/ax/be, 3G/4G/LTE/5G/6G/7G/8G, 3GPP,Bluetooth, BLE, Zigbee, RFID, UWB, WiMax) compliant signal, protocolsignal, standard frame,beacon/pilot/probe/enquiry/acknowledgement/handshake/synchronizationsignal, management/control/data frame, management/control/data signal,standardized wireless/cellular communication protocol, reference signal,source signal, motion probe/detection/sensing signal, and/or series ofsignals. The wireless signal may comprise a line-of-sight (LOS), and/ora non-LOS component (or path/link). Each CI may beextracted/generated/computed/sensed at a layer (e.g. PHY/MAC layer inOSI model) of Type 2 device and may be obtained by an application (e.g.software, firmware, driver, app, wireless monitoring software/system).

The wireless multipath channel may comprise: a communication channel,analog frequency channel (e.g. with analog carrier frequency near700/800/900 MHz, 1.8/1.8/2.4/3/5/6/27/60 GHz), coded channel (e.g. inCDMA), and/or channel of a wireless network/system (e.g. WLAN, WiFi,mesh, LTE, 4G/5G, Bluetooth, Zigbee, UWB, RFID, microwave). It maycomprise more than one channel. The channels may be consecutive (e.g.with adjacent/overlapping bands) or non-consecutive channels (e.g.non-overlapping WiFi channels, one at 2.4 GHz and one at 5 GHz).

The TSCI may be extracted from the wireless signal at a layer of theType 2 device (e.g. a layer of OSI reference model, physical layer, datalink layer, logical link control layer, media access control (MAC)layer, network layer, transport layer, session layer, presentationlayer, application layer, TCP/IP layer, internet layer, link layer). TheTSCI may be extracted from a derived signal (e.g. baseband signal,motion detection signal, motion sensing signal) derived from thewireless signal (e.g. RF signal). It may be (wireless) measurementssensed by the communication protocol (e.g. standardized protocol) usingexisting mechanism (e.g. wireless/cellular communicationstandard/network, 3G/LTE/4G/5G/6G/7G/8G, WiFi, IEEE 802.11/15/16). Thederived signal may comprise a packet with at least one of: a preamble, aheader and a payload (e.g. for data/control/management in wirelesslinks/networks). The TSCI may be extracted from a probe signal (e.g.training sequence, STF, LTF, L-STF, L-LTF, L-SIG, HE-STF, HE-LTF, CEF)in the packet. A motion detection/sensing signal may berecognized/identified base on the probe signal. The packet may be astandard-compliant protocol frame, management frame, control frame, dataframe, sounding frame, excitation frame, illumination frame, null dataframe, beacon frame, pilot frame, probe frame, request frame, responseframe, association frame, reassociation frame, disassociation frame,authentication frame, action frame, report frame, poll frame,announcement frame, extension frame, enquiry frame, acknowledgementframe, RTS frame, CTS frame, QoS frame, CF-Poll frame, CF-Ack frame,block acknowledgement frame, reference frame, training frame, and/orsynchronization frame.

The packet may comprise a control data and/or a motion detection probe.A data (e.g. ID/parameters/characteristics/settings/controlsignal/command/instruction/notification/broadcasting-related informationof the Type 1 device) may be obtained from the payload. The wirelesssignal may be transmitted by the Type 1 device. It may be received bythe Type 2 device. A database (e.g. in local server, hub device, cloudserver, storage network) may be used to store the TSCI, characteristics,STI, signatures, patterns, behaviors, trends, parameters, analytics,output responses, identification information, user information, deviceinformation, channel information, venue (e.g. map, environmental model,network, proximity devices/networks) information, task information,class/category information, presentation (e.g. UI) information, and/orother information.

The Type 1/Type 2 device may comprise at least one of: electronics,circuitry, transmitter (TX)/receiver (RX)/transceiver, RF interface,“Origin Satellite”/“Tracker Bot”, unicast/multicast/broadcasting device,wireless source device, source/destination device, wireless node, hubdevice, target device, motion detection device, sensor device,remote/wireless sensor device, wireless communication device,wireless-enabled device, standard compliant device, and/or receiver. TheType 1 (or Type 2) device may be heterogeneous because, when there aremore than one instances of Type 1 (or Type 2) device, they may havedifferent circuitry, enclosure, structure, purpose, auxiliaryfunctionality, chip/IC, processor, memory, software, firmware, networkconnectivity, antenna, brand, model, appearance, form, shape, color,material, and/or specification. The Type 1/Type 2 device may comprise:access point, router, mesh router, internet-of-things (IoT) device,wireless terminal, one or more radio/RF subsystem/wireless interface(e.g. 2.4 GHz radio, 5 GHz radio, front haul radio, backhaul radio),modem, RF front end, RF/radio chip or integrated circuit (IC).

At least one of: Type 1 device, Type 2 device, a link between them, theobject, the characteristics, the STI, the monitoring of the motion, andthe task may be associated with an identification (ID) such as UUID. TheType 1/Type 2/another device mayobtain/store/retrieve/access/preprocess/condition/process/analyze/monitor/applythe TSCI. The Type 1 and Type 2 devices may communicate network trafficin another channel (e.g. Ethernet, HDMI, USB, Bluetooth, BLE, WiFi, LTE,other network, the wireless multipath channel) in parallel to thewireless signal. The Type 2 device may passively observe/monitor/receivethe wireless signal from the Type 1 device in the wireless multipathchannel without establishing connection (e.g.association/authentication) with, or requesting service from, the Type 1device.

The transmitter (i.e. Type 1 device) may function as (play role of)receiver (i.e. Type 2 device) temporarily, sporadically, continuously,repeatedly, interchangeably, alternately, simultaneously, concurrently,and/or contemporaneously; and vice versa. A device may function as Type1 device (transmitter) and/or Type 2 device (receiver) temporarily,sporadically, continuously, repeatedly, simultaneously, concurrently,and/or contemporaneously. There may be multiple wireless nodes eachbeing Type 1 (TX) and/or Type 2 (RX) device. A TSCI may be obtainedbetween every two nodes when they exchange/communicate wireless signals.The characteristics and/or STI of the object may be monitoredindividually based on a TSCI, or jointly based on two or more (e.g. all)TSCI.

The motion of the object may be monitored actively (in that Type 1device, Type 2 device, or both, are wearable of/associated with theobject) and/or passively (in that both Type 1 and Type 2 devices are notwearable of/associated with the object). It may be passive because theobject may not be associated with the Type 1 device and/or the Type 2device. The object (e.g. user, an automated guided vehicle or AGV) maynot need to carry/install any wearables/fixtures (i.e. the Type 1 deviceand the Type 2 device are not wearable/attached devices that the objectneeds to carry in order perform the task). It may be active because theobject may be associated with either the Type 1 device and/or the Type 2device. The object may carry (or installed) a wearable/a fixture (e.g.the Type 1 device, the Type 2 device, a device communicatively coupledwith either the Type 1 device or the Type 2 device).

The presentation may be visual, audio, image, video, animation,graphical presentation, text, etc. A computation of the task may beperformed by a processor (or logic unit) of the Type 1 device, aprocessor (or logic unit) of an IC of the Type 1 device, a processor (orlogic unit) of the Type 2 device, a processor of an IC of the Type 2device, a local server, a cloud server, a data analysis subsystem, asignal analysis subsystem, and/or another processor. The task may beperformed with/without reference to a wireless fingerprint or a baseline(e.g. collected, processed, computed, transmitted and/or stored in atraining phase/survey/current survey/previous survey/recentsurvey/initial wireless survey, a passive fingerprint), a training, aprofile, a trained profile, a static profile, a survey, an initialwireless survey, an initial setup, an installation, a re-training, anupdating and a reset.

The Type 1 device (TX device) may comprise at least one heterogeneouswireless transmitter. The Type 2 device (RX device) may comprise atleast one heterogeneous wireless receiver. The Type 1 device and theType 2 device may be collocated. The Type 1 device and the Type 2 devicemay be the same device. Any device may have a data processingunit/apparatus, a computing unit/system, a network unit/system, aprocessor (e.g. logic unit), a memory communicatively coupled with theprocessor, and a set of instructions stored in the memory to be executedby the processor. Some processors, memories and sets of instructions maybe coordinated.

There may be multiple Type 1 devices interacting (e.g. communicating,exchange signal/control/notification/other data) with the same Type 2device (or multiple Type 2 devices), and/or there may be multiple Type 2devices interacting with the same Type 1 device. The multiple Type 1devices/Type 2 devices may be synchronized and/or asynchronous, withsame/different window width/size and/or time shift, same/differentsynchronized start time, synchronized end time, etc. Wireless signalssent by the multiple Type 1 devices may be sporadic, temporary,continuous, repeated, synchronous, simultaneous, concurrent, and/orcontemporaneous. The multiple Type 1 devices/Type 2 devices may operateindependently and/or collaboratively. A Type 1 and/or Type 2 device mayhave/comprise/be heterogeneous hardware circuitry (e.g. a heterogeneouschip or a heterogeneous IC capable of generating/receiving the wirelesssignal, extracting CI from received signal, or making the CI available).They may be communicatively coupled to same or different servers (e.g.cloud server, edge server, local server, hub device).

Operation of one device may be based on operation, state, internalstate, storage, processor, memory output, physical location, computingresources, network of another device. Difference devices may communicatedirectly, and/or via another device/server/hub device/cloud server. Thedevices may be associated with one or more users, with associatedsettings. The settings may be chosen once, pre-programmed, and/orchanged (e.g. adjusted, varied, modified)/varied over time. There may beadditional steps in the method. The steps and/or the additional steps ofthe method may be performed in the order shown or in another order. Anysteps may be performed in parallel, iterated, or otherwise repeated orperformed in another manner. A user may be human, adult, older adult,man, woman, juvenile, child, baby, pet, animal, creature, machine,computer module/software, etc.

In the case of one or multiple Type 1 devices interacting with one ormultiple Type 2 devices, any processing (e.g. time domain, frequencydomain) may be different for different devices. The processing may bebased on locations, orientation, direction, roles, user-relatedcharacteristics, settings, configurations, available resources,available bandwidth, network connection, hardware, software, processor,co-processor, memory, battery life, available power, antennas, antennatypes, directional/unidirectional characteristics of the antenna, powersetting, and/or other parameters/characteristics of the devices.

The wireless receiver (e.g. Type 2 device) may receive the signal and/oranother signal from the wireless transmitter (e.g. Type 1 device). Thewireless receiver may receive another signal from another wirelesstransmitter (e.g. a second Type 1 device). The wireless transmitter maytransmit the signal and/or another signal to another wireless receiver(e.g. a second Type 2 device). The wireless transmitter, wirelessreceiver, another wireless receiver and/or another wireless transmittermay be moving with the object and/or another object. The another objectmay be tracked.

The Type 1 and/or Type 2 device may be capable of wirelessly couplingwith at least two Type 2 and/or Type 1 devices. The Type 1 device may becaused/controlled to switch/establish wireless coupling (e.g.association, authentication) from the Type 2 device to a second Type 2device at another location in the venue. Similarly, the Type 2 devicemay be caused/controlled to switch/establish wireless coupling from theType 1 device to a second Type 1 device at yet another location in thevenue. The switching may be controlled by a server (or a hub device),the processor, the Type 1 device, the Type 2 device, and/or anotherdevice. The radio used before and after switching may be different. Asecond wireless signal (second signal) may be caused to be transmittedbetween the Type 1 device and the second Type 2 device (or between theType 2 device and the second Type 1 device) through the channel. Asecond TSCI of the channel extracted from the second signal may beobtained. The second signal may be the first signal. Thecharacteristics, STI and/or another quantity of the object may bemonitored based on the second TSCI. The Type 1 device and the Type 2device may be the same. The characteristics, STI and/or another quantitywith different time stamps may form a waveform. The waveform may bedisplayed in the presentation.

The wireless signal and/or another signal may have data embedded. Thewireless signal may be a series of probe signals (e.g. a repeatedtransmission of probe signals, a re-use of one or more probe signals).The probe signals may change/vary over time. A probe signal may be astandard compliant signal, protocol signal, standardized wirelessprotocol signal, control signal, data signal, wireless communicationnetwork signal, cellular network signal, WiFi signal, LTE/5G/6G/7Gsignal, reference signal, beacon signal, motion detection signal, and/ormotion sensing signal. A probe signal may be formatted according to awireless network standard (e.g. WiFi), a cellular network standard (e.g.LTE/5G/6G), or another standard. A probe signal may comprise a packetwith a header and a payload. A probe signal may have data embedded. Thepayload may comprise data. A probe signal may be replaced by a datasignal. The probe signal may be embedded in a data signal. The wirelessreceiver, wireless transmitter, another wireless receiver and/or anotherwireless transmitter may be associated with at least one processor,memory communicatively coupled with respective processor, and/orrespective set of instructions stored in the memory which when executedcause the processor to perform any and/or all steps needed to determinethe STI (e.g. motion information), initial STI, initial time, direction,instantaneous location, instantaneous angle, and/or speed, of theobject.

The processor, the memory and/or the set of instructions may beassociated with the Type 1 device, one of the at least one Type 2device, the object, a device associated with the object, another deviceassociated with the venue, a cloud server, a hub device, and/or anotherserver.

The Type 1 device may transmit the signal in a broadcasting manner to atleast one Type 2 device(s) through the channel in the venue. The signalis transmitted without the Type 1 device establishing wirelessconnection (e.g. association, authentication) with any Type 2 device,and without any Type 2 device requesting services from the Type 1device. The Type 1 device may transmit to a particular media accesscontrol (MAC) address common for more than one Type 2 devices. Each Type2 device may adjust its MAC address to the particular MAC address. Theparticular MAC address may be associated with the venue. The associationmay be recorded in an association table of an Association Server (e.g.hub device). The venue may be identified by the Type 1 device, a Type 2device and/or another device based on the particular MAC address, theseries of probe signals, and/or the at least one TSCI extracted from theprobe signals.

For example, a Type 2 device may be moved to a new location in the venue(e.g. from another venue). The Type 1 device may be newly set up in thevenue such that the Type 1 and Type 2 devices are not aware of eachother. During set up, the Type 1 device may beinstructed/guided/caused/controlled (e.g. using dummy receiver, usinghardware pin setting/connection, using stored setting, using localsetting, using remote setting, using downloaded setting, using hubdevice, or using server) to send the series of probe signals to theparticular MAC address. Upon power up, the Type 2 device may scan forprobe signals according to a table of MAC addresses (e.g. stored in adesignated source, server, hub device, cloud server) that may be usedfor broadcasting at different locations (e.g. different MAC address usedfor different venue such as house, office, enclosure, floor,multi-storey building, store, airport, mall, stadium, hall, station,subway, lot, area, zone, region, district, city, country, continent).When the Type 2 device detects the probe signals sent to the particularMAC address, the Type 2 device can use the table to identify the venuebased on the MAC address.

A location of a Type 2 device in the venue may be computed based on theparticular MAC address, the series of probe signals, and/or the at leastone TSCI obtained by the Type 2 device from the probe signals. Thecomputing may be performed by the Type 2 device.

The particular MAC address may be changed (e.g. adjusted, varied,modified) over time. It may be changed according to a time table, rule,policy, mode, condition, situation and/or change. The particular MACaddress may be selected based on availability of the MAC address, apre-selected list, collision pattern, traffic pattern, data trafficbetween the Type 1 device and another device, effective bandwidth,random selection, and/or a MAC address switching plan. The particularMAC address may be the MAC address of a second wireless device (e.g. adummy receiver, or a receiver that serves as a dummy receiver).

The Type 1 device may transmit the probe signals in a channel selectedfrom a set of channels. At least one CI of the selected channel may beobtained by a respective Type 2 device from the probe signal transmittedin the selected channel.

The selected channel may be changed (e.g. adjusted, varied, modified)over time. The change may be according to a time table, rule, policy,mode, condition, situation, and/or change. The selected channel may beselected based on availability of channels, random selection, apre-selected list, co-channel interference, inter-channel interference,channel traffic pattern, data traffic between the Type 1 device andanother device, effective bandwidth associated with channels, securitycriterion, channel switching plan, a criterion, a quality criterion, asignal quality condition, and/or consideration.

The particular MAC address and/or an information of the selected channelmay be communicated between the Type 1 device and a server (e.g. hubdevice) through a network. The particular MAC address and/or theinformation of the selected channel may also be communicated between aType 2 device and a server (e.g. hub device) through another network.The Type 2 device may communicate the particular MAC address and/or theinformation of the selected channel to another Type 2 device (e.g. viamesh network, Bluetooth, WiFi, NFC, ZigBee, etc.). The particular MACaddress and/or selected channel may be chosen by a server (e.g. hubdevice). The particular MAC address and/or selected channel may besignaled in an announcement channel by the Type 1 device, the Type 2device and/or a server (e.g. hub device). Before being communicated, anyinformation may be pre-processed.

Wireless connection (e.g. association, authentication) between the Type1 device and another wireless device may be established (e.g. using asignal handshake). The Type 1 device may send a first handshake signal(e.g. sounding frame, probe signal, request-to-send RTS) to the anotherdevice. The another device may reply by sending a second handshakesignal (e.g. a command, or a clear-to-send CTS) to the Type 1 device,triggering the Type 1 device to transmit the signal (e.g. series ofprobe signals) in the broadcasting manner to multiple Type 2 deviceswithout establishing connection with any Type 2 device. The secondhandshake signals may be a response or an acknowledge (e.g. ACK) to thefirst handshake signal. The second handshake signal may contain a datawith information of the venue, and/or the Type 1 device. The anotherdevice may be a dummy device with a purpose (e.g. primary purpose,secondary purpose) to establish the wireless connection with the Type 1device, to receive the first signal, and/or to send the second signal.The another device may be physically attached to the Type 1 device.

In another example, the another device may send a third handshake signalto the Type 1 device triggering the Type 1 device to broadcast thesignal (e.g. series of probe signals) to multiple Type 2 devices withoutestablishing connection (e.g. association, authentication) with any Type2 device. The Type 1 device may reply to the third special signal bytransmitting a fourth handshake signal to the another device. Theanother device may be used to trigger more than one Type 1 devices tobroadcast. The triggering may be sequential, partially sequential,partially parallel, or fully parallel. The another device may have morethan one wireless circuitries to trigger multiple transmitters inparallel. Parallel trigger may also be achieved using at least one yetanother device to perform the triggering (similar to what as the anotherdevice does) in parallel to the another device. The another device maynot communicate (or suspend communication) with the Type 1 device afterestablishing connection with the Type 1 device. Suspended communicationmay be resumed. The another device may enter an inactive mode,hibernation mode, sleep mode, stand-by mode, low-power mode, OFF modeand/or power-down mode, after establishing the connection with the Type1 device. The another device may have the particular MAC address so thatthe Type 1 device sends the signal to the particular MAC address. TheType 1 device and/or the another device may be controlled and/orcoordinated by a first processor associated with the Type 1 device, asecond processor associated with the another device, a third processorassociated with a designated source and/or a fourth processor associatedwith another device. The first and second processors may coordinate witheach other.

A first series of probe signals may be transmitted by a first antenna ofthe Type 1 device to at least one first Type 2 device through a firstchannel in a first venue. A second series of probe signals may betransmitted by a second antenna of the Type 1 device to at least onesecond Type 2 device through a second channel in a second venue. Thefirst series and the second series may/may not be different. The atleast one first Type 2 device may/may not be different from the at leastone second Type 2 device. The first and/or second series of probesignals may be broadcasted without connection (e.g. association,authentication) established between the Type 1 device and any Type 2device. The first and second antennas may be same/different.

The two venues may have different sizes, shape, multipathcharacteristics. The first and second venues may overlap. The respectiveimmediate areas around the first and second antennas may overlap. Thefirst and second channels may be same/different. For example, the firstone may be WiFi while the second may be LTE. Or, both may be WiFi, butthe first one may be 2.4 GHz WiFi and the second may be 5 GHz WiFi. Or,both may be 2.4 GHz WiFi, but have different channel numbers, SSIDnames, and/or WiFi settings.

Each Type 2 device may obtain at least one TSCI from the respectiveseries of probe signals, the CI being of the respective channel betweenthe Type 2 device and the Type 1 device. Some first Type 2 device(s) andsome second Type 2 device(s) may be the same. The first and secondseries of probe signals may be synchronous/asynchronous. A probe signalmay be transmitted with data or replaced by a data signal. The first andsecond antennas may be the same.

The first series of probe signals may be transmitted at a first rate(e.g. 30 Hz). The second series of probe signals may be transmitted at asecond rate (e.g. 200 Hz). The first and second rates may besame/different. The first and/or second rate may be changed (e.g.adjusted, varied, modified) over time. The change may be according to atime table, rule, policy, mode, condition, situation, and/or change. Anyrate may be changed (e.g. adjusted, varied, modified) over time.

The first and/or second series of probe signals may be transmitted to afirst MAC address and/or second MAC address respectively. The two MACaddresses may be same/different. The first series of probe signals maybe transmitted in a first channel. The second series of probe signalsmay be transmitted in a second channel. The two channels may besame/different. The first or second MAC address, first or second channelmay be changed over time. Any change may be according to a time table,rule, policy, mode, condition, situation, and/or change.

The Type 1 device and another device may be controlled and/orcoordinated, physically attached, or may be of/in/of a common device.They may be controlled by/connected to a common data processor, or maybe connected to a common bus interconnect/network/LAN/Bluetoothnetwork/NFC network/BLE network/wired network/wireless network/meshnetwork/mobile network/cloud. They may share a common memory, or beassociated with a common user, user device, profile, account, identity(ID), identifier, household, house, physical address, location,geographic coordinate, IP subnet, SSID, home device, office device,and/or manufacturing device.

Each Type 1 device may be a signal source of a set of respective Type 2devices (i.e. it sends a respective signal (e.g. respective series ofprobe signals) to the set of respective Type 2 devices). Each respectiveType 2 device chooses the Type 1 device from among all Type 1 devices asits signal source. Each Type 2 device may choose asynchronously. Atleast one TSCI may be obtained by each respective Type 2 device from therespective series of probe signals from the Type 1 device, the CI beingof the channel between the Type 2 device and the Type 1 device.

The respective Type 2 device chooses the Type 1 device from among allType 1 devices as its signal source based on identity (ID) or identifierof Type 1/Type 2 device, task to be performed, past signal source,history (e.g. of past signal source, Type 1 device, another Type 1device, respective Type 2 receiver, and/or another Type 2 receiver),threshold for switching signal source, and/or information of a user,account, access info, parameter, characteristics, and/or signal strength(e.g. associated with the Type 1 device and/or the respective Type 2receiver).

Initially, the Type 1 device may be signal source of a set of initialrespective Type 2 devices (i.e. the Type 1 device sends a respectivesignal (series of probe signals) to the set of initial respective Type 2devices) at an initial time. Each initial respective Type 2 devicechooses the Type 1 device from among all Type 1 devices as its signalsource.

The signal source (Type 1 device) of a particular Type 2 device may bechanged (e.g. adjusted, varied, modified) when (1) time interval betweentwo adjacent probe signals (e.g. between current probe signal andimmediate past probe signal, or between next probe signal and currentprobe signal) received from current signal source of the Type 2 deviceexceeds a first threshold; (2) signal strength associated with currentsignal source of the Type 2 device is below a second threshold; (3) aprocessed signal strength associated with current signal source of theType 2 device is below a third threshold, the signal strength processedwith low pass filter, band pass filter, median filter, moving averagefilter, weighted averaging filter, linear filter and/or non-linearfilter; and/or (4) signal strength (or processed signal strength)associated with current signal source of the Type 2 device is below afourth threshold for a significant percentage of a recent time window(e.g. 70%, 80%, 90%). The percentage may exceed a fifth threshold. Thefirst, second, third, fourth and/or fifth thresholds may be timevarying.

Condition (1) may occur when the Type 1 device and the Type 2 devicebecome progressively far away from each other, such that some probesignal from the Type 1 device becomes too weak and is not received bythe Type 2 device. Conditions (2)-(4) may occur when the two devicesbecome far from each other such that the signal strength becomes veryweak.

The signal source of the Type 2 device may not change if other Type 1devices have signal strength weaker than a factor (e.g. 1, 1.1, 1.2, or1.5) of the current signal source.

If the signal source is changed (e.g. adjusted, varied, modified), thenew signal source may take effect at a near future time (e.g. therespective next time). The new signal source may be the Type 1 devicewith strongest signal strength, and/or processed signal strength. Thecurrent and new signal source may be same/different.

A list of available Type 1 devices may be initialized and maintained byeach Type 2 device. The list may be updated by examining signal strengthand/or processed signal strength associated with the respective set ofType 1 devices. A Type 2 device may choose between a first series ofprobe signals from a first Type 1 device and a second series of probesignals from a second Type 1 device based on: respective probe signalrate, MAC addresses, channels, characteristics/properties/states, taskto be performed by the Type 2 device, signal strength of first andsecond series, and/or another consideration.

The series of probe signals may be transmitted at a regular rate (e.g.100 Hz). The series of probe signals may be scheduled at a regularinterval (e.g. 0.01 s for 100 Hz), but each probe signal may experiencesmall time perturbation, perhaps due to timing requirement, timingcontrol, network control, handshaking, message passing, collisionavoidance, carrier sensing, congestion, availability of resources,and/or another consideration.

The rate may be changed (e.g. adjusted, varied, modified). The changemay be according to a time table (e.g. changed once every hour), rule,policy, mode, condition and/or change (e.g. changed whenever some eventoccur). For example, the rate may normally be 100 Hz, but changed to1000 Hz in demanding situations, and to 1 Hz in low power/standbysituation. The probe signals may be sent in burst.

The probe signal rate may change based on a task performed by the Type 1device or Type 2 device (e.g. a task may need 100 Hz normally and 1000Hz momentarily for 20 seconds). In one example, the transmitters (Type 1devices), receivers (Type 2 device), and associated tasks may beassociated adaptively (and/or dynamically) to classes (e.g. classes thatare: low-priority, high-priority, emergency, critical, regular,privileged, non-subscription, subscription, paying, and/or non-paying).A rate (of a transmitter) may be adjusted for the sake of some class(e.g. high priority class). When the need of that class changes, therate may be changed (e.g. adjusted, varied, modified). When a receiverhas critically low power, the rate may be reduced to reduce powerconsumption of the receiver to respond to the probe signals. In oneexample, probe signals may be used to transfer power wirelessly to areceiver (Type 2 device), and the rate may be adjusted to control theamount of power transferred to the receiver.

The rate may be changed by (or based on): a server (e.g. hub device),the Type 1 device and/or the Type 2 device. Control signals may becommunicated between them. The server may monitor, track, forecastand/or anticipate the needs of the Type 2 device and/or the tasksperformed by the Type 2 device, and may control the Type 1 device tochange the rate. The server may make scheduled changes to the rateaccording to a time table. The server may detect an emergency situationand change the rate immediately. The server may detect a developingcondition and adjust the rate gradually.

The characteristics and/or STI (e.g. motion information) may bemonitored individually based on a TSCI associated with a particular Type1 device and a particular Type 2 device, and/or monitored jointly basedon any TSCI associated with the particular Type 1 device and any Type 2device, and/or monitored jointly based on any TSCI associated with theparticular Type 2 device and any Type 1 device, and/or monitoredglobally based on any TSCI associated with any Type 1 device and anyType 2 device. Any joint monitoring may be associated with: a user, useraccount, profile, household, map of venue, environmental model of thevenue, and/or user history, etc.

A first channel between a Type 1 device and a Type 2 device may bedifferent from a second channel between another Type 1 device andanother Type 2 device. The two channels may be associated with differentfrequency bands, bandwidth, carrier frequency, modulation, wirelessstandards, coding, encryption, payload characteristics, networks,network ID, SSID, network characteristics, network settings, and/ornetwork parameters, etc.

The two channels may be associated with different kinds of wirelesssystem (e.g. two of the following: WiFi, LTE, LTE-A, LTE-U, 2.5G, 3G,3.5G, 4G, beyond 4G, 5G, 6G, 7G, a cellular network standard, UMTS,3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, 802.11 system,802.15 system, 802.16 system, mesh network, Zigbee, NFC, WiMax,Bluetooth, BLE, RFID, UWB, microwave system, radar like system). Forexample, one is WiFi and the other is LTE.

The two channels may be associated with similar kinds of wirelesssystem, but in different network. For example, the first channel may beassociated with a WiFi network named “Pizza and Pizza” in the 2.4 GHzband with a bandwidth of 20 MHz while the second may be associated witha WiFi network with SSID of “StarBud hotspot” in the 5 GHz band with abandwidth of 40 MHz. The two channels may be different channels in samenetwork (e.g. the “StarBud hotspot” network).

In one embodiment, a wireless monitoring system may comprise training aclassifier of multiple events in a venue based on training TSCIassociated with the multiple events. A CI or TSCI associated with anevent may be considered/may comprise a wirelesssample/characteristics/fingerprint associated with the event (and/or thevenue, the environment, the object, the motion of the object, astate/emotional state/mentalstate/condition/stage/gesture/gait/action/movement/activity/dailyactivity/history/event of the object, etc.).

For each of the multiple known events happening in the venue in arespective training (e.g. surveying, wireless survey, initial wirelesssurvey) time period associated with the known event, a respectivetraining wireless signal (e.g. a respective series of training probesignals) may be transmitted by an antenna of a first Type 1heterogeneous wireless device using a processor, a memory and a set ofinstructions of the first Type 1 device to at least one first Type 2heterogeneous wireless device through a wireless multipath channel inthe venue in the respective training time period.

At least one respective time series of training CI (training TSCI) maybe obtained asynchronously by each of the at least one first Type 2device from the (respective) training signal. The CI may be CI of thechannel between the first Type 2 device and the first Type 1 device inthe training time period associated with the known event. The at leastone training TSCI may be preprocessed. The training may be a wirelesssurvey (e.g. during installation of Type 1 device and/or Type 2 device).

For a current event happening in the venue in a current time period, acurrent wireless signal (e.g. a series of current probe signals) may betransmitted by an antenna of a second Type 1 heterogeneous wirelessdevice using a processor, a memory and a set of instructions of thesecond Type 1 device to at least one second Type 2 heterogeneouswireless device through the channel in the venue in the current timeperiod associated with the current event.

At least one time series of current CI (current TSCI) may be obtainedasynchronously by each of the at least one second Type 2 device from thecurrent signal (e.g. the series of current probe signals). The CI may beCI of the channel between the second Type 2 device and the second Type 1device in the current time period associated with the current event. Theat least one current TSCI may be preprocessed.

The classifier may be applied to classify at least one current TSCIobtained from the series of current probe signals by the at least onesecond Type 2 device, to classify at least one portion of a particularcurrent TSCI, and/or to classify a combination of the at least oneportion of the particular current TSCI and another portion of anotherTSCI. The classifier may partition TSCI (or the characteristics/STI orother analytics or output responses) into clusters and associate theclusters to specificevents/objects/subjects/locations/movements/activities. Labels/tags maybe generated for the clusters. The clusters may be stored and retrieved.The classifier may be applied to associate the current TSCI (orcharacteristics/STI or the other analytics/output response, perhapsassociated with a current event) with: a cluster, a known/specificevent, a class/category/group/grouping/list/cluster/set of knownevents/subjects/locations/movements/activities, an unknown event, aclass/category/group/grouping/list/cluster/set of unknownevents/subjects/locations/movements/activities, and/or anotherevent/subject/location/movement/activity/class/category/group/grouping/list/cluster/set.Each TSCI may comprise at least one CI each associated with a respectivetimestamp. Two TSCI associated with two Type 2 devices may be differentwith different: starting time, duration, stopping time, amount of CI,sampling frequency, sampling period. Their CI may have differentfeatures. The first and second Type 1 devices may be at same location inthe venue. They may be the same device. The at least one second Type 2device (or their locations) may be a permutation of the at least onefirst Type 2 device (or their locations). A particular second Type 2device and a particular first Type 2 device may be the same device.

A subset of the first Type 2 device and a subset of the second Type 2device may be the same. The at least one second Type 2 device and/or asubset of the at least one second Type 2 device may be a subset of theat least one first Type 2 device. The at least one first Type 2 deviceand/or a subset of the at least one first Type 2 device may be apermutation of a subset of the at least one second Type 2 device. The atleast one second Type 2 device and/or a subset of the at least onesecond Type 2 device may be a permutation of a subset of the at leastone first Type 2 device. The at least one second Type 2 device and/or asubset of the at least one second Type 2 device may be at samerespective location as a subset of the at least one first Type 2 device.The at least one first Type 2 device and/or a subset of the at least onefirst Type 2 device may be at same respective location as a subset ofthe at least one second Type 2 device.

The antenna of the Type 1 device and the antenna of the second Type 1device may be at same location in the venue. Antenna(s) of the at leastone second Type 2 device and/or antenna(s) of a subset of the at leastone second Type 2 device may be at same respective location asrespective antenna(s) of a subset of the at least one first Type 2device. Antenna(s) of the at least one first Type 2 device and/orantenna(s) of a subset of the at least one first Type 2 device may be atsame respective location(s) as respective antenna(s) of a subset of theat least one second Type 2 device.

A first section of a first time duration of the first TSCI and a secondsection of a second time duration of the second section of the secondTSCI may be aligned. A map between items of the first section and itemsof the second section may be computed. The first section may comprise afirst segment (e.g. subset) of the first TSCI with a firststarting/ending time, and/or another segment (e.g. subset) of aprocessed first TSCI. The processed first TSCI may be the first TSCIprocessed by a first operation. The second section may comprise a secondsegment (e.g. subset) of the second TSCI with a second starting time anda second ending time, and another segment (e.g. subset) of a processedsecond TSCI. The processed second TSCI may be the second TSCI processedby a second operation. The first operation and/or the second operationmay comprise: subsampling, re-sampling, interpolation, filtering,transformation, feature extraction, pre-processing, and/or anotheroperation.

A first item of the first section may be mapped to a second item of thesecond section. The first item of the first section may also be mappedto another item of the second section. Another item of the first sectionmay also be mapped to the second item of the second section. The mappingmay be one-to-one, one-to-many, many-to-one, many-to-many. At least onefunction of at least one of: the first item of the first section of thefirst TSCI, another item of the first TSCI, timestamp of the first item,time difference of the first item, time differential of the first item,neighboring timestamp of the first item, another timestamp associatedwith the first item, the second item of the second section of the secondTSCI, another item of the second TSCI, timestamp of the second item,time difference of the second item, time differential of the seconditem, neighboring timestamp of the second item, and another timestampassociated with the second item, may satisfy at least one constraint.

One constraint may be that a difference between the timestamp of thefirst item and the timestamp of the second item may be upper-bounded byan adaptive (and/or dynamically adjusted) upper threshold andlower-bounded by an adaptive lower threshold.

The first section may be the entire first TSCI. The second section maybe the entire second TSCI. The first time duration may be equal to thesecond time duration. A section of a time duration of a TSCI may bedetermined adaptively (and/or dynamically). A tentative section of theTSCI may be computed. A starting time and an ending time of a section(e.g. the tentative section, the section) may be determined. The sectionmay be determined by removing a beginning portion and an ending portionof the tentative section. A beginning portion of a tentative section maybe determined as follows. Iteratively, items of the tentative sectionwith increasing timestamp may be considered as a current item, one itemat a time.

In each iteration, at least one activity measure/index may be computedand/or considered. The at least one activity measure may be associatedwith at least one of: the current item associated with a currenttimestamp, past items of the tentative section with timestamps notlarger than the current timestamp, and/or future items of the tentativesection with timestamps not smaller than the current timestamp. Thecurrent item may be added to the beginning portion of the tentativesection if at least one criterion (e.g. quality criterion, signalquality condition) associated with the at least one activity measure issatisfied.

The at least one criterion associated with the activity measure maycomprise at least one of: (a) the activity measure is smaller than anadaptive (e.g. dynamically adjusted) upper threshold, (b) the activitymeasure is larger than an adaptive lower threshold, (c) the activitymeasure is smaller than an adaptive upper threshold consecutively for atleast a predetermined amount of consecutive timestamps, (d) the activitymeasure is larger than an adaptive lower threshold consecutively for atleast another predetermined amount of consecutive timestamps, (e) theactivity measure is smaller than an adaptive upper thresholdconsecutively for at least a predetermined percentage of thepredetermined amount of consecutive timestamps, (f) the activity measureis larger than an adaptive lower threshold consecutively for at leastanother predetermined percentage of the another predetermined amount ofconsecutive timestamps, (g) another activity measure associated withanother timestamp associated with the current timestamp is smaller thananother adaptive upper threshold and larger than another adaptive lowerthreshold, (h) at least one activity measure associated with at leastone respective timestamp associated with the current timestamp issmaller than respective upper threshold and larger than respective lowerthreshold, (i) percentage of timestamps with associated activity measuresmaller than respective upper threshold and larger than respective lowerthreshold in a set of timestamps associated with the current timestampexceeds a threshold, and (j) another criterion (e.g. a qualitycriterion, signal quality condition).

An activity measure/index associated with an item at time T1 maycomprise at least one of: (1) a first function of the item at time T1and an item at time T1−D1, wherein D1 is a pre-determined positivequantity (e.g. a constant time offset), (2) a second function of theitem at time T1 and an item at time T1+D1, (3) a third function of theitem at time T1 and an item at time T2, wherein T2 is a pre-determinedquantity (e.g. a fixed initial reference time; T2 may be changed (e.g.adjusted, varied, modified) over time; T2 may be updated periodically;T2 may be the beginning of a time period and T1 may be a sliding time inthe time period), and (4) a fourth function of the item at time T1 andanother item.

At least one of: the first function, the second function, the thirdfunction, and/or the fourth function may be a function (e.g. F(X, Y, . .. )) with at least two arguments: X and Y. The two arguments may bescalars. The function (e.g. F) may be a function of at least one of: X,Y, (X−Y), (Y−X), abs(X−Y), X{circumflex over ( )}a, Y{circumflex over( )}b, abs(X{circumflex over ( )}a−Y{circumflex over ( )}b),(X−Y){circumflex over ( )}a, (X/Y), (X+a)/(Y+b), (X{circumflex over( )}a/Y{circumflex over ( )}b), and ((X/Y){circumflex over ( )}a−b),wherein a and b are may be some predetermined quantities. For example,the function may simply be abs(X−Y), or (X−Y){circumflex over ( )}2,(X−Y){circumflex over ( )}4. The function may be a robust function. Forexample, the function may be (X−Y){circumflex over ( )}2 when abs (X−Y)is less than a threshold T, and (X−Y)+a when abs(X−Y) is larger than T.Alternatively, the function may be a constant when abs(X−Y) is largerthan T. The function may also be bounded by a slowly increasing functionwhen abs(X−y) is larger than T, so that outliers cannot severely affectthe result. Another example of the function may be (abs(X/Y)−a), wherea=1. In this way, if X=Y (i.e. no change or no activity), the functionwill give a value of 0. If X is larger than Y, (X/Y) will be larger than1 (assuming X and Y are positive) and the function will be positive. Andif X is less than Y, (X/Y) will be smaller than 1 and the function willbe negative. In another example, both arguments X and Y may be n-tuplessuch that X=(x_1, x_2, . . . , x_n) and Y=(y_1, y_2, . . . , y_n). Thefunction may be a function of at least one of: x_i, y_i, (x_i−y_i),(y_i−x_i), abs(x_i−y_i), x_i{circumflex over ( )}a, y_i{circumflex over( )}b, abs(x_i{circumflex over ( )}a−y_i{circumflex over ( )}b),(x_i−y_i){circumflex over ( )}a, (x_i/y_i), (x_i+a)/(y_i+b),(x_i{circumflex over ( )}a/y_i{circumflex over ( )}b), and((x_i/y_i){circumflex over ( )}a−b), wherein i is a component index ofthe n-tuple X and Y, and 1<=i<=n, e.g. component index of x_1 is i=1,component index of x_2 is i=2. The function may comprise acomponent-by-component summation of another function of at least one ofthe following: x_i, y_i, (x_i−y_i), (y_i−x_i), abs(x_i−y_i),x_i{circumflex over ( )}a, y_i{circumflex over ( )}b, abs(x_i{circumflexover ( )}a−y_i{circumflex over ( )}b), (x_i−y_i){circumflex over ( )}a,(x_i/y_i), (x_i+a)/(y_i+b), (x_i{circumflex over ( )}a/y_i{circumflexover ( )}b), and ((x_i/y_i){circumflex over ( )}a−b), wherein i is thecomponent index of the n-tuple X and Y. For example, the function may bein a form of sum_{i=1}{circumflex over ( )}n (abs(x_i/y_i)−1)/n, orsum_{i=1}{circumflex over ( )}n w_i*(abs(x_i/y_i)−1), where w_i is someweight for component i.

The map may be computed using dynamic time warping (DTW). The DTW maycomprise a constraint on at least one of: the map, the items of thefirst TSCI, the items of the second TSCI, the first time duration, thesecond time duration, the first section, and/or the second section.Suppose in the map, the i{circumflex over ( )}{th} domain item is mappedto the j{circumflex over ( )}{th} range item. The constraint may be onadmissible combination of i and j (constraint on relationship between iand j). Mismatch cost between a first section of a first time durationof a first TSCI and a second section of a second time duration of asecond TSCI may be computed.

The first section and the second section may be aligned such that a mapcomprising more than one links may be established between first items ofthe first TSCI and second items of the second TSCI. With each link, oneof the first items with a first timestamp may be associated with one ofthe second items with a second timestamp. A mismatch cost between thealigned first section and the aligned second section may be computed.The mismatch cost may comprise a function of: an item-wise cost betweena first item and a second item associated by a particular link of themap, and a link-wise cost associated with the particular link of themap.

The aligned first section and the aligned second section may berepresented respectively as a first vector and a second vector of samevector length. The mismatch cost may comprise at least one of: an innerproduct, inner-product-like quantity, quantity based on correlation,correlation indicator, quantity based on covariance, discriminatingscore, distance, Euclidean distance, absolute distance, Lk distance(e.g. L1, L2, . . . ), weighted distance, distance-like quantity and/oranother similarity value, between the first vector and the secondvector. The mismatch cost may be normalized by the respective vectorlength.

A parameter derived from the mismatch cost between the first section ofthe first time duration of the first TSCI and the second section of thesecond time duration of the second TSCI may be modeled with astatistical distribution. At least one of: a scale parameter, locationparameter and/or another parameter, of the statistical distribution maybe estimated.

The first section of the first time duration of the first TSCI may be asliding section of the first TSCI. The second section of the second timeduration of the second TSCI may be a sliding section of the second TSCI.

A first sliding window may be applied to the first TSCI and acorresponding second sliding window may be applied to the second TSCI.The first sliding window of the first TSCI and the corresponding secondsliding window of the second TSCI may be aligned.

Mismatch cost between the aligned first sliding window of the first TSCIand the corresponding aligned second sliding window of the second TSCImay be computed. The current event may be associated with at least oneof: the known event, the unknown event and/or the another event, basedon the mismatch cost.

The classifier may be applied to at least one of: each first section ofthe first time duration of the first TSCI, and/or each second section ofthe second time duration of the second TSCI, to obtain at least onetentative classification results. Each tentative classification resultmay be associated with a respective first section and a respectivesecond section.

The current event may be associated with at least one of: the knownevent, the unknown event, a class/category/group/grouping/list/set ofunknown events, and/or the another event, based on the mismatch cost.The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on a largestnumber of tentative classification results in more than one sections ofthe first TSCI and corresponding more than sections of the second TSCI.For example, the current event may be associated with a particular knownevent if the mismatch cost points to the particular known event for Nconsecutive times (e.g. N=10). In another example, the current event maybe associated with a particular known event if the percentage ofmismatch cost within the immediate past N consecutive N pointing to theparticular known event exceeds a certain threshold (e.g. >80%).

In another example, the current event may be associated with a knownevent that achieves smallest mismatch cost for the most times within atime period. The current event may be associated with a known event thatachieves smallest overall mismatch cost, which is a weighted average ofat least one mismatch cost associated with the at least one firstsections. The current event may be associated with a particular knownevent that achieves smallest of another overall cost. The current eventmay be associated with the “unknown event” if none of the known eventsachieve mismatch cost lower than a first threshold T1 in a sufficientpercentage of the at least one first section. The current event may alsobe associated with the “unknown event” if none of the events achieve anoverall mismatch cost lower than a second threshold T2. The currentevent may be associated with at least one of: the known event, theunknown event and/or the another event, based on the mismatch cost andadditional mismatch cost associated with at least one additional sectionof the first TSCI and at least one additional section of the secondTSCI. The known events may comprise at least one of: a door closedevent, door open event, window closed event, window open event,multi-state event, on-state event, off-state event, intermediate stateevent, continuous state event, discrete state event, human-presentevent, human-absent event, sign-of-life-present event, and/or asign-of-life-absent event.

A projection for each CI may be trained using a dimension reductionmethod based on the training TSCI. The dimension reduction method maycomprise at least one of: principal component analysis (PCA), PCA withdifferent kernel, independent component analysis (ICA), Fisher lineardiscriminant, vector quantization, supervised learning, unsupervisedlearning, self-organizing maps, auto-encoder, neural network, deepneural network, and/or another method. The projection may be applied toat least one of: the training TSCI associated with the at least oneevent, and/or the current TSCI, for the classifier.

The classifier of the at least one event may be trained based on theprojection and the training TSCI associated with the at least one event.The at least one current TSCI may be classified/categorized based on theprojection and the current TSCI. The projection may be re-trained usingat least one of: the dimension reduction method, and another dimensionreduction method, based on at least one of: the training TSCI, at leastone current TSCI before retraining the projection, and/or additionaltraining TSCI. The another dimension reduction method may comprise atleast one of: principal component analysis (PCA), PCA with differentkernels, independent component analysis (ICA), Fisher lineardiscriminant, vector quantization, supervised learning, unsupervisedlearning, self-organizing maps, auto-encoder, neural network, deepneural network, and/or yet another method. The classifier of the atleast one event may be re-trained based on at least one of: there-trained projection, the training TSCI associated with the at leastone events, and/or at least one current TSCI. The at least one currentTSCI may be classified based on: the re-trained projection, there-trained classifier, and/or the current TSCI.

Each CI may comprise a vector of complex values. Each complex value maybe preprocessed to give the magnitude of the complex value. Each CI maybe preprocessed to give a vector of non-negative real numbers comprisingthe magnitude of corresponding complex values. Each training TSCI may beweighted in the training of the projection. The projection may comprisemore than one projected components. The projection may comprise at leastone most significant projected component. The projection may comprise atleast one projected component that may be beneficial for the classifier.

Channel/channel information/venue/spatial-temporal info/motion/object

The channel information (CI) may be associated with/may comprise signalstrength, signal amplitude, signal phase, spectral power measurement,modem parameters (e.g. used in relation to modulation/demodulation indigital communication systems such as WiFi, 4G/LTE), dynamic beamforminginformation, transfer function components, radio state (e.g. used indigital communication systems to decode digital data, basebandprocessing state, RF processing state, etc.), measurable variables,sensed data, coarse-grained/fine-grained information of a layer (e.g.physical layer, data link layer, MAC layer, etc.), digital setting, gainsetting, RF filter setting, RF front end switch setting, DC offsetsetting, DC correction setting, IQ compensation setting, effect(s) onthe wireless signal by the environment (e.g. venue) during propagation,transformation of an input signal (the wireless signal transmitted bythe Type 1 device) to an output signal (the wireless signal received bythe Type 2 device), a stable behavior of the environment, a stateprofile, wireless channel measurements, received signal strengthindicator (RS SI), channel state information (CSI), channel impulseresponse (CIR), channel frequency response (CFR), characteristics offrequency components (e.g. subcarriers) in a bandwidth, channelcharacteristics, channel filter response, timestamp, auxiliaryinformation, data, meta data, user data, account data, access data,security data, session data, status data, supervisory data, householddata, identity (ID), identifier, device data, network data, neighborhooddata, environment data, real-time data, sensor data, stored data,encrypted data, compressed data, protected data, and/or another channelinformation. Each CI may be associated with a time stamp, and/or anarrival time. A CSI can be used to equalize/undo/minimize/reduce themultipath channel effect (of the transmission channel) to demodulate asignal similar to the one transmitted by the transmitter through themultipath channel. The CI may be associated with information associatedwith a frequency band, frequency signature, frequency phase, frequencyamplitude, frequency trend, frequency characteristics, frequency-likecharacteristics, time domain element, frequency domain element,time-frequency domain element, orthogonal decomposition characteristics,and/or non-orthogonal decomposition characteristics of the signalthrough the channel. The TSCI may be a stream of wireless signals (e.g.CI).

The CI may be preprocessed, processed, postprocessed, stored (e.g. inlocal memory, portable/mobile memory, removable memory, storage network,cloud memory, in a volatile manner, in a non-volatile manner),retrieved, transmitted and/or received. One or more modem parametersand/or radio state parameters may be held constant. The modem parametersmay be applied to a radio subsystem. The modem parameters may representa radio state. A motion detection signal (e.g. baseband signal, and/orpacket decoded/demodulated from the baseband signal, etc.) may beobtained by processing (e.g. down-converting) the first wireless signal(e.g. RF/WiFi/LTE/5G signal) by the radio subsystem using the radiostate represented by the stored modem parameters. The modemparameters/radio state may be updated (e.g. using previous modemparameters or previous radio state). Both the previous and updated modemparameters/radio states may be applied in the radio subsystem in thedigital communication system. Both the previous and updated modemparameters/radio states may be compared/analyzed/processed/monitored inthe task.

The channel information may also be modem parameters (e.g. stored orfreshly computed) used to process the wireless signal. The wirelesssignal may comprise a plurality of probe signals. The same modemparameters may be used to process more than one probe signals. The samemodem parameters may also be used to process more than one wirelesssignals. The modem parameters may comprise parameters that indicatesettings or an overall configuration for the operation of a radiosubsystem or a baseband subsystem of a wireless sensor device (or both).The modem parameters may include one or more of: a gain setting, an RFfilter setting, an RF front end switch setting, a DC offset setting, oran IQ compensation setting for a radio subsystem, or a digital DCcorrection setting, a digital gain setting, and/or a digital filteringsetting (e.g. for a baseband subsystem). The CI may also be associatedwith information associated with a time period, time signature,timestamp, time amplitude, time phase, time trend, and/or timecharacteristics of the signal. The CI may be associated with informationassociated with a time-frequency partition, signature, amplitude, phase,trend, and/or characteristics of the signal. The CI may be associatedwith a decomposition of the signal. The CI may be associated withinformation associated with a direction, angle of arrival (AoA), angleof a directional antenna, and/or a phase of the signal through thechannel. The CI may be associated with attenuation patterns of thesignal through the channel. Each CI may be associated with a Type 1device and a Type 2 device. Each CI may be associated with an antenna ofthe Type 1 device and an antenna of the Type 2 device.

The CI may be obtained from a communication hardware (e.g. of Type 2device, or Type 1 device) that is capable of providing the CI. Thecommunication hardware may be a WiFi-capable chip/IC (integratedcircuit), chip compliant with a 802.11 or 802.16 or anotherwireless/radio standard, next generation WiFi-capable chip, LTE-capablechip, 5G-capable chip, 6G/7G/8G-capable chip, Bluetooth-enabled chip,NFC (near field communication)-enabled chip, BLE (Bluetooth lowpower)-enabled chip, UWB chip, another communication chip (e.g. Zigbee,WiMax, mesh network), etc. The communication hardware computes the CIand stores the CI in a buffer memory and make the CI available forextraction. The CI may comprise data and/or at least one matricesrelated to channel state information (CSI). The at least one matricesmay be used for channel equalization, and/or beam forming, etc. Thechannel may be associated with a venue. The attenuation may be due tosignal propagation in the venue, signalpropagating/reflection/refraction/diffraction through/at/around air(e.g. air of venue), refraction medium/reflection surface such as wall,doors, furniture, obstacles and/or barriers, etc. The attenuation may bedue to reflection at surfaces and obstacles (e.g. reflection surface,obstacle) such as floor, ceiling, furniture, fixtures, objects, people,pets, etc. Each CI may be associated with a timestamp. Each CI maycomprise N1 components (e.g. N1 frequency domain components in CFR, N1time domain components in CIR, or N1 decomposition components). Eachcomponent may be associated with a component index. Each component maybe a real, imaginary, or complex quantity, magnitude, phase, flag,and/or set. Each CI may comprise a vector or matrix of complex numbers,a set of mixed quantities, and/or a multi-dimensional collection of atleast one complex numbers.

Components of a TSCI associated with a particular component index mayform a respective component time series associated with the respectiveindex. A TSCI may be divided into N1 component time series. Eachrespective component time series is associated with a respectivecomponent index. The characteristics/STI of the motion of the object maybe monitored based on the component time series. In one example, one ormore ranges of CI components (e.g. one range being from component 11 tocomponent 23, a second range being from component 44 to component 50,and a third range having only one component) may be selected based onsome criteria/cost function/signal quality metric (e.g. based onsignal-to-noise ratio, and/or interference level) for furtherprocessing.

A component-wise characteristic of a component-feature time series of aTSCI may be computed. The component-wise characteristics may be a scalar(e.g. energy) or a function with a domain and a range (e.g. anautocorrelation function, transform, inverse transform). Thecharacteristics/STI of the motion of the object may be monitored basedon the component-wise characteristics. A total characteristics (e.g.aggregate characteristics) of the TSCI may be computed based on thecomponent-wise characteristics of each component time series of theTSCI. The total characteristics may be a weighted average of thecomponent-wise characteristics. The characteristics/STI of the motion ofthe object may be monitored based on the total characteristics. Anaggregate quantity may be a weighted average of individual quantities.

The Type 1 device and Type 2 device may support WiFi, WiMax, 3G/beyond3G, 4G/beyond 4G, LTE, LTE-A, 5G, 6G, 7G, Bluetooth, NFC, BLE, Zigbee,UWB, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, meshnetwork, proprietary wireless system, IEEE 802.11 standard, 802.15standard, 802.16 standard, 3GPP standard, and/or another wirelesssystem.

A common wireless system and/or a common wireless channel may be sharedby the Type 1 transceiver and/or the at least one Type 2 transceiver.The at least one Type 2 transceiver may transmit respective signalcontemporaneously (or: asynchronously, synchronously, sporadically,continuously, repeatedly, concurrently, simultaneously and/ortemporarily) using the common wireless system and/or the common wirelesschannel. The Type 1 transceiver may transmit a signal to the at leastone Type 2 transceiver using the common wireless system and/or thecommon wireless channel.

Each Type 1 device and Type 2 device may have at least onetransmitting/receiving antenna. Each CI may be associated with one ofthe transmitting antenna of the Type 1 device and one of the receivingantenna of the Type 2 device. Each pair of a transmitting antenna and areceiving antenna may be associated with a link, a path, a communicationpath, signal hardware path, etc. For example, if the Type 1 device has M(e.g. 3) transmitting antennas, and the Type 2 device has N (e.g. 2)receiving antennas, there may be M×N (e.g. 3×2=6) links or paths. Eachlink or path may be associated with a TSCI.

The at least one TSCI may correspond to various antenna pairs betweenthe Type 1 device and the Type 2 device. The Type 1 device may have atleast one antenna. The Type 2 device may also have at least one antenna.Each TSCI may be associated with an antenna of the Type 1 device and anantenna of the Type 2 device. Averaging or weighted averaging overantenna links may be performed. The averaging or weighted averaging maybe over the at least one TSCI. The averaging may optionally be performedon a subset of the at least one TSCI corresponding to a subset of theantenna pairs.

Timestamps of CI of a portion of a TSCI may be irregular and may becorrected so that corrected timestamps of time-corrected CI may beuniformly spaced in time. In the case of multiple Type 1 devices and/ormultiple Type 2 devices, the corrected timestamp may be with respect tothe same or different clock. An original timestamp associated with eachof the CI may be determined. The original timestamp may not be uniformlyspaced in time. Original timestamps of all CI of the particular portionof the particular TSCI in the current sliding time window may becorrected so that corrected timestamps of time-corrected CI may beuniformly spaced in time.

The characteristics and/or STI (e.g. motion information) may comprise:location, location coordinate, change in location, position (e.g.initial position, new position), position on map, height, horizontallocation, vertical location, distance, displacement, speed,acceleration, rotational speed, rotational acceleration, direction,angle of motion, azimuth, direction of motion, rotation, path,deformation, transformation, shrinking, expanding, gait, gait cycle,head motion, repeated motion, periodic motion, pseudo-periodic motion,impulsive motion, sudden motion, fall-down motion, transient motion,behavior, transient behavior, period of motion, frequency of motion,time trend, temporal profile, temporal characteristics, occurrence,change, temporal change, change of CI, change in frequency, change intiming, change of gait cycle, timing, starting time, initiating time,ending time, duration, history of motion, motion type, motionclassification, frequency, frequency spectrum, frequencycharacteristics, presence, absence, proximity, approaching, receding,identity/identifier of the object, composition of the object, headmotion rate, head motion direction, mouth-related rate, eye-relatedrate, breathing rate, heart rate, tidal volume, depth of breath, inhaletime, exhale time, inhale time to exhale time ratio, airflow rate, heartheat-to-beat interval, heart rate variability, hand motion rate, handmotion direction, leg motion, body motion, walking rate, hand motionrate, positional characteristics, characteristics associated withmovement (e.g. change in position/location) of the object, tool motion,machine motion, complex motion, and/or combination of multiple motions,event, signal statistics, signal dynamics, anomaly, motion statistics,motion parameter, indication of motion detection, motion magnitude,motion phase, similarity score, distance score, Euclidean distance,weighted distance, L_1 norm, L_2 norm, L_k norm for k>2, statisticaldistance, correlation, correlation indicator, auto-correlation,covariance, auto-covariance, cross-covariance, inner product, outerproduct, motion signal transformation, motion feature, presence ofmotion, absence of motion, motion localization, motion identification,motion recognition, presence of object, absence of object, entrance ofobject, exit of object, a change of object, motion cycle, motion count,gait cycle, motion rhythm, deformation motion, gesture, handwriting,head motion, mouth motion, heart motion, internal organ motion, motiontrend, size, length, area, volume, capacity, shape, form, tag,starting/initiating location, ending location, starting/initiatingquantity, ending quantity, event, fall-down event, security event,accident event, home event, office event, factory event, warehouseevent, manufacturing event, assembly line event, maintenance event,car-related event, navigation event, tracking event, door event,door-open event, door-close event, window event, window-open event,window-close event, repeatable event, one-time event, consumed quantity,unconsumed quantity, state, physical state, health state, well-beingstate, emotional state, mental state, another event, analytics, outputresponses, and/or another information. The characteristics and/or STImay be computed/monitored based on a feature computed from a CI or aTSCI (e.g. feature computation/extraction). A static segment or profile(and/or a dynamic segment/profile) may beidentified/computed/analyzed/monitored/extracted/obtained/marked/presented/indicated/highlighted/stored/communicatedbased on an analysis of the feature. The analysis may comprise a motiondetection/movement assessment/presence detection. Computational workloadmay be shared among the Type 1 device, the Type 2 device and anotherprocessor.

The Type 1 device and/or Type 2 device may be a local device. The localdevice may be: a smart phone, smart device, TV, sound bar, set-top box,access point, router, repeater, wireless signal repeater/extender,remote control, speaker, fan, refrigerator, microwave, oven, coffeemachine, hot water pot, utensil, table, chair, light, lamp, door lock,camera, microphone, motion sensor, security device, fire hydrant, garagedoor, switch, power adapter, computer, dongle, computer peripheral,electronic pad, sofa, tile, accessory, home device, vehicle device,office device, building device, manufacturing device, watch, glasses,clock, television, oven, air-conditioner, accessory, utility, appliance,smart machine, smart vehicle, internet-of-thing (IoT) device,internet-enabled device, computer, portable computer, tablet, smarthouse, smart office, smart building, smart parking lot, smart system,and/or another device.

Each Type 1 device may be associated with a respective identifier (e.g.ID). Each Type 2 device may also be associated with a respectiveidentify (ID). The ID may comprise: numeral, combination of text andnumbers, name, password, account, account ID, web link, web address,index to some information, and/or another ID. The ID may be assigned.The ID may be assigned by hardware (e.g. hardwired, via dongle and/orother hardware), software and/or firmware. The ID may be stored (e.g. indatabase, in memory, in server (e.g. hub device), in the cloud, storedlocally, stored remotely, stored permanently, stored temporarily) andmay be retrieved. The ID may be associated with at least one record,account, user, household, address, phone number, social security number,customer number, another ID, another identifier, timestamp, and/orcollection of data. The ID and/or part of the ID of a Type 1 device maybe made available to a Type 2 device. The ID may be used forregistration, initialization, communication, identification,verification, detection, recognition, authentication, access control,cloud access, networking, social networking, logging, recording,cataloging, classification, tagging, association, pairing, transaction,electronic transaction, and/or intellectual property control, by theType 1 device and/or the Type 2 device.

The object may be person, user, subject, passenger, child, older person,baby, sleeping baby, baby in vehicle, patient, worker, high-valueworker, expert, specialist, waiter, customer in mall, traveler inairport/train station/bus terminal/shipping terminals,staff/worker/customer service personnel infactory/mall/supermarket/office/workplace, serviceman in sewage/airventilation system/lift well, lifts in lift wells, elevator, inmate,people to be tracked/monitored, animal, plant, living object, pet, dog,cat, smart phone, phone accessory, computer, tablet, portable computer,dongle, computing accessory, networked devices, WiFi devices, IoTdevices, smart watch, smart glasses, smart devices, speaker, keys, smartkey, wallet, purse, handbag, backpack, goods, cargo, luggage, equipment,motor, machine, air conditioner, fan, air conditioning equipment, lightfixture, moveable light, television, camera, audio and/or videoequipment, stationary, surveillance equipment, parts, signage, tool,cart, ticket, parking ticket, toll ticket, airplane ticket, credit card,plastic card, access card, food packaging, utensil, table, chair,cleaning equipment/tool, vehicle, car, cars in parking facilities,merchandise in warehouse/store/supermarket/distribution center, boat,bicycle, airplane, drone, remote control car/plane/boat, robot,manufacturing device, assembly line, material/unfinishedpart/robot/wagon/transports on factory floor, object to be tracked inairport/shopping mart/supermarket, non-object, absence of an object,presence of an object, object with form, object with changing form,object with no form, mass of fluid, mass of liquid, mass of gas/smoke,fire, flame, electromagnetic (EM) source, EM medium, and/or anotherobject.

The object itself may be communicatively coupled with some network, suchas WiFi, MiFi, 3G/4G/LTE/5G/6G/7G, Bluetooth, NFC, BLE, WiMax, Zigbee,UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, mesh network,adhoc network, and/or other network. The object itself may be bulky withAC power supply, but is moved during installation, cleaning,maintenance, renovation, etc. It may also be installed in moveableplatform such as lift, pad, movable, platform, elevator, conveyor belt,robot, drone, forklift, car, boat, vehicle, etc. The object may havemultiple parts, each part with different movement (e.g. change inposition/location). For example, the object may be a person walkingforward. While walking, his left hand and right hand may move indifferent direction, with different instantaneous speed, acceleration,motion, etc.

The wireless transmitter (e.g. Type 1 device), the wireless receiver(e.g. Type 2 device), another wireless transmitter and/or anotherwireless receiver may move with the object and/or another object (e.g.in prior movement, current movement and/or future movement. They may becommunicatively coupled to one or more nearby device. They may transmitTSCI and/or information associated with the TSCI to the nearby device,and/or each other. They may be with the nearby device. The wirelesstransmitter and/or the wireless receiver may be part of a small (e.g.coin-size, cigarette box size, or even smaller), light-weight portabledevice. The portable device may be wirelessly coupled with a nearbydevice.

The nearby device may be smart phone, iPhone, Android phone, smartdevice, smart appliance, smart vehicle, smart gadget, smart TV, smartrefrigerator, smart speaker, smart watch, smart glasses, smart pad,iPad, computer, wearable computer, notebook computer, gateway. Thenearby device may be connected to a cloud server, local server (e.g. hubdevice) and/or other server via internet, wired internet connectionand/or wireless internet connection. The nearby device may be portable.The portable device, the nearby device, a local server (e.g. hub device)and/or a cloud server may share the computation and/or storage for atask (e.g. obtain TSCI, determine characteristics/STI of the objectassociated with the movement (e.g. change in position/location) of theobject, computation of time series of power (e.g. signal strength)information, determining/computing the particular function, searchingfor local extremum, classification, identifying particular value of timeoffset, de-noising, processing, simplification, cleaning, wireless smartsensing task, extract CI from signal, switching, segmentation, estimatetrajectory/path/track, process the map, processing trajectory/path/trackbased on environment models/constraints/limitations, correction,corrective adjustment, adjustment, map-based (or model-based)correction, detecting error, checking for boundary hitting,thresholding) and information (e.g. TSCI). The nearby device may/may notmove with the object. The nearby device may be portable/notportable/moveable/non-moveable. The nearby device may use battery power,solar power, AC power and/or other power source. The nearby device mayhave replaceable/non-replaceable battery, and/orrechargeable/non-rechargeable battery. The nearby device may be similarto the object. The nearby device may have identical (and/or similar)hardware and/or software to the object. The nearby device may be a smartdevice, network enabled device, device with connection toWiFi/3G/4G/5G/6G/Zigbee/Bluetooth/NFC/UMTS/3GPP/GSM/EDGE/TDMA/FDMA/CDMA/WCDMA/TD-SCDMA/adhocnetwork/other network, smart speaker, smart watch, smart clock, smartappliance, smart machine, smart equipment, smart tool, smart vehicle,internet-of-thing (IoT) device, internet-enabled device, computer,portable computer, tablet, and another device. The nearby device and/orat least one processor associated with the wireless receiver, thewireless transmitter, the another wireless receiver, the anotherwireless transmitter and/or a cloud server (in the cloud) may determinethe initial STI of the object. Two or more of them may determine theinitial spatial-temporal info jointly. Two or more of them may shareintermediate information in the determination of the initial STI (e.g.initial position).

In one example, the wireless transmitter (e.g. Type 1 device, or TrackerBot) may move with the object. The wireless transmitter may send thesignal to the wireless receiver (e.g. Type 2 device, or Origin Register)or determining the initial STI (e.g. initial position) of the object.The wireless transmitter may also send the signal and/or another signalto another wireless receiver (e.g. another Type 2 device, or anotherOrigin Register) for the monitoring of the motion (spatial-temporalinfo) of the object. The wireless receiver may also receive the signaland/or another signal from the wireless transmitter and/or the anotherwireless transmitter for monitoring the motion of the object. Thelocation of the wireless receiver and/or the another wireless receivermay be known. In another example, the wireless receiver (e.g. Type 2device, or Tracker Bot) may move with the object. The wireless receivermay receive the signal transmitted from the wireless transmitter (e.g.Type 1 device, or Origin Register) for determining the initialspatial-temporal info (e.g. initial position) of the object. Thewireless receiver may also receive the signal and/or another signal fromanother wireless transmitter (e.g. another Type 1 device, or anotherOrigin Register) for the monitoring of the current motion (e.g.spatial-temporal info) of the object. The wireless transmitter may alsotransmit the signal and/or another signal to the wireless receiverand/or the another wireless receiver (e.g. another Type 2 device, oranother Tracker Bot) for monitoring the motion of the object. Thelocation of the wireless transmitter and/or the another wirelesstransmitter may be known.

The venue may be a space such as a sensing area, room, house, office,property, workplace, hallway, walkway, lift, lift well, escalator,elevator, sewage system, air ventilations system, staircase, gatheringarea, duct, air duct, pipe, tube, enclosed space, enclosed structure,semi-enclosed structure, enclosed area, area with at least one wall,plant, machine, engine, structure with wood, structure with glass,structure with metal, structure with walls, structure with doors,structure with gaps, structure with reflection surface, structure withfluid, building, roof top, store, factory, assembly line, hotel room,museum, classroom, school, university, government building, warehouse,garage, mall, airport, train station, bus terminal, hub, transportationhub, shipping terminal, government facility, public facility, school,university, entertainment facility, recreational facility, hospital,pediatric/neonatal wards, seniors home, elderly care facility, geriatricfacility, community center, stadium, playground, park, field, sportsfacility, swimming facility, track and/or field, basketball court,tennis court, soccer stadium, baseball stadium, gymnasium, hall, garage,shopping mart, mall, supermarket, manufacturing facility, parkingfacility, construction site, mining facility, transportation facility,highway, road, valley, forest, wood, terrain, landscape, den, patio,land, path, amusement park, urban area, rural area, suburban area,metropolitan area, garden, square, plaza, music hall, downtown facility,over-air facility, semi-open facility, closed area, train platform,train station, distribution center, warehouse, store, distributioncenter, storage facility, underground facility, space (e.g. aboveground, outer-space) facility, floating facility, cavern, tunnelfacility, indoor facility, open-air facility, outdoor facility with somewalls/doors/reflective barriers, open facility, semi-open facility, car,truck, bus, van, container, ship/boat, submersible, train, tram,airplane, vehicle, mobile home, cave, tunnel, pipe, channel,metropolitan area, downtown area with relatively tall buildings, valley,well, duct, pathway, gas line, oil line, water pipe, network ofinterconnecting pathways/alleys/roads/tubes/cavities/caves/pipe-likestructure/air space/fluid space, human body, animal body, body cavity,organ, bone, teeth, soft tissue, hard tissue, rigid tissue, non-rigidtissue, blood/body fluid vessel, windpipe, air duct, den, etc. The venuemay be indoor space, outdoor space, The venue may include both theinside and outside of the space. For example, the venue may include boththe inside of a building and the outside of the building. For example,the venue can be a building that has one floor or multiple floors, and aportion of the building can be underground. The shape of the buildingcan be, e.g., round, square, rectangular, triangle, or irregular-shaped.These are merely examples. The disclosure can be used to detect eventsin other types of venue or spaces.

The wireless transmitter (e.g. Type 1 device) and/or the wirelessreceiver (e.g. Type 2 device) may be embedded in a portable device (e.g.a module, or a device with the module) that may move with the object(e.g. in prior movement and/or current movement). The portable devicemay be communicatively coupled with the object using a wired connection(e.g. through USB, microUSB, Firewire, HDMI, serial port, parallel port,and other connectors) and/or a connection (e.g. Bluetooth, Bluetooth LowEnergy (BLE), WiFi, LTE, NFC, ZigBee). The portable device may be alightweight device. The portable may be powered by battery, rechargeablebattery and/or AC power. The portable device may be very small (e.g. atsub-millimeter scale and/or sub-centimeter scale), and/or small (e.g.coin-size, card-size, pocket-size, or larger). The portable device maybe large, sizable, and/or bulky (e.g. heavy machinery to be installed).The portable device may be a WiFi hotspot, access point, mobile WiFi(MiFi), dongle with USB/micro USB/Firewire/other connector, smartphone,portable computer, computer, tablet, smart device, internet-of-thing(IoT) device, WiFi-enabled device, LTE-enabled device, a smart watch,smart glass, smart mirror, smart antenna, smart battery, smart light,smart pen, smart ring, smart door, smart window, smart clock, smallbattery, smart wallet, smart belt, smart handbag, smartclothing/garment, smart ornament, smart packaging, smartpaper/book/magazine/poster/printed matter/signage/display/lightedsystem/lighting system, smart key/tool, smartbracelet/chain/necklace/wearable/accessory, smart pad/cushion, smarttile/block/brick/building material/other material, smart garbagecan/waste container, smart food carriage/storage, smart ball/racket,smart chair/sofa/bed, smart shoe/footwear/carpet/mat/shoe rack, smartglove/hand wear/ring/hand ware, smarthat/headwear/makeup/sticker/tattoo, smart mirror, smart toy, smart pill,smart utensil, smart bottle/food container, smart tool, smart device,IoT device, WiFi enabled device, network enabled device, 3G/4G/5G/6Genabled device, UNITS devices, 3GPP devices, GSM devices, EDGE devices,TDMA devices, FDMA devices, CDMA devices, WCDMA devices, TD-SCDMAdevices, embeddable device, implantable device, air conditioner,refrigerator, heater, furnace, furniture, oven, cooking device,television/set-top box (STB)/DVD player/audio player/video player/remotecontrol, hi-fi, audio device, speaker, lamp/light, wall, door, window,roof, roof tile/shingle/structure/atticstructure/device/feature/installation/fixtures, lawn mower/gardentools/yard tools/mechanics tools/garage tools/, garbage can/container,20-ft/40-ft container, storage container,factory/manufacturing/production device, repair tools, fluid container,machine, machinery to be installed, vehicle, cart, wagon, warehousevehicle, car, bicycle, motorcycle, boat, vessel, airplane,basket/box/bag/bucket/container, smartplate/cup/bowl/pot/mat/utensils/kitchen tools/kitchen devices/kitchenaccessories/cabinets/tables/chairs/tiles/lights/water pipes/taps/gasrange/oven/dishwashing machine/etc. The portable device may have abattery that may be replaceable, irreplaceable, rechargeable, and/ornon-rechargeable. The portable device may be wirelessly charged. Theportable device may be a smart payment card. The portable device may bea payment card used in parking lots, highways, entertainment parks, orother venues/facilities that need payment. The portable device may havean identity (ID)/identifier as described above.

An event may be monitored based on the TSCI. The event may be an objectrelated event, such as fall-down of the object (e.g. an person and/or asick person), rotation, hesitation, pause, impact (e.g. a person hittinga sandbag, door, window, bed, chair, table, desk, cabinet, box, anotherperson, animal, bird, fly, table, chair, ball, bowling ball, tennisball, football, soccer ball, baseball, basketball, volley ball),two-body action (e.g. a person letting go a balloon, catching a fish,molding a clay, writing a paper, person typing on a computer), carmoving in a garage, person carrying a smart phone and walking around anairport/mall/government building/office/etc., autonomous moveableobject/machine moving around (e.g. vacuum cleaner, utility vehicle, car,drone, self-driving car).

The task or the wireless smart sensing task may comprise: objectdetection, presence detection, proximity detection, object recognition,activity recognition, object verification, object counting, dailyactivity monitoring, well-being monitoring, vital sign monitoring,health condition monitoring, baby monitoring, elderly monitoring, sleepmonitoring, sleep stage monitoring, walking monitoring, exercisemonitoring, tool detection, tool recognition, tool verification, patientdetection, patient monitoring, patient verification, machine detection,machine recognition, machine verification, human detection, humanrecognition, human verification, baby detection, baby recognition, babyverification, human breathing detection, human breathing recognition,human breathing estimation, human breathing verification, human heartbeat detection, human heart beat recognition, human heart beatestimation, human heart beat verification, fall-down detection,fall-down recognition, fall-down estimation, fall-down verification,emotion detection, emotion recognition, emotion estimation, emotionverification, motion detection, motion degree estimation, motionrecognition, motion estimation, motion verification, periodic motiondetection, periodic motion recognition, periodic motion estimation,periodic motion verification, repeated motion detection, repeated motionrecognition, repeated motion estimation, repeated motion verification,stationary motion detection, stationary motion recognition, stationarymotion estimation, stationary motion verification, cyclo-stationarymotion detection, cyclo-stationary motion recognition, cyclo-stationarymotion estimation, cyclo-stationary motion verification, transientmotion detection, transient motion recognition, transient motionestimation, transient motion verification, trend detection, trendrecognition, trend estimation, trend verification, breathing detection,breathing recognition, breathing estimation, breathing estimation, humanbiometrics detection, human biometric recognition, human biometricsestimation, human biometrics verification, environment informaticsdetection, environment informatics recognition, environment informaticsestimation, environment informatics verification, gait detection, gaitrecognition, gait estimation, gait verification, gesture detection,gesture recognition, gesture estimation, gesture verification, machinelearning, supervised learning, unsupervised learning, semi-supervisedlearning, clustering, feature extraction, featuring training, principalcomponent analysis, eigen-decomposition, frequency decomposition, timedecomposition, time-frequency decomposition, functional decomposition,other decomposition, training, discriminative training, supervisedtraining, unsupervised training, semi-supervised training, neuralnetwork, sudden motion detection, fall-down detection, danger detection,life-threat detection, regular motion detection, stationary motiondetection, cyclo-stationary motion detection, intrusion detection,suspicious motion detection, security, safety monitoring, navigation,guidance, map-based processing, map-based correction, model-basedprocessing/correction, irregularity detection, locationing, roomsensing, tracking, multiple object tracking, indoor tracking, indoorposition, indoor navigation, energy management, power transfer, wirelesspower transfer, object counting, car tracking in parking garage,activating a device/system (e.g. security system, access system, alarm,siren, speaker, television, entertaining system, camera,heater/air-conditioning (HVAC) system, ventilation system, lightingsystem, gaming system, coffee machine, cooking device, cleaning device,housekeeping device), geometry estimation, augmented reality, wirelesscommunication, data communication, signal broadcasting, networking,coordination, administration, encryption, protection, cloud computing,other processing and/or other task. The task may be performed by theType 1 device, the Type 2 device, another Type 1 device, another Type 2device, a nearby device, a local server (e.g. hub device), edge server,a cloud server, and/or another device. The task may be based on TSCIbetween any pair of Type 1 device and Type 2 device. A Type 2 device maybe a Type 1 device, and vice versa. A Type 2 device may play/perform therole (e.g. functionality) of Type 1 device temporarily, continuously,sporadically, simultaneously, and/or contemporaneously, and vice versa.A first part of the task may comprise at least one of: preprocessing,processing, signal conditioning, signal processing, post-processing,processingsporadically/continuously/simultaneously/contemporaneously/dynamically/adaptive/on-demand/as-needed,calibrating, denoising, feature extraction, coding, encryption,transformation, mapping, motion detection, motion estimation, motionchange detection, motion pattern detection, motion pattern estimation,motion pattern recognition, vital sign detection, vital sign estimation,vital sign recognition, periodic motion detection, periodic motionestimation, repeated motion detection/estimation, breathing ratedetection, breathing rate estimation, breathing pattern detection,breathing pattern estimation, breathing pattern recognition, heart beatdetection, heart beat estimation, heart pattern detection, heart patternestimation, heart pattern recognition, gesture detection, gestureestimation, gesture recognition, speed detection, speed estimation,object locationing, object tracking, navigation, accelerationestimation, acceleration detection, fall-down detection, changedetection, intruder (and/or illegal action) detection, baby detection,baby monitoring, patient monitoring, object recognition, wireless powertransfer, and/or wireless charging.

A second part of the task may comprise at least one of: a smart hometask, smart office task, smart building task, smart factory task (e.g.manufacturing using a machine or an assembly line), smartinternet-of-thing (IoT) task, smart system task, smart home operation,smart office operation, smart building operation, smart manufacturingoperation (e.g. moving supplies/parts/raw material to a machine/anassembly line), IoT operation, smart system operation, turning on alight, turning off the light, controlling the light in at least one of:a room, region, and/or the venue, playing a sound clip, playing thesound clip in at least one of: the room, the region, and/or the venue,playing the sound clip of at least one of: a welcome, greeting,farewell, first message, and/or a second message associated with thefirst part of the task, turning on an appliance, turning off theappliance, controlling the appliance in at least one of: the room, theregion, and/or the venue, turning on an electrical system, turning offthe electrical system, controlling the electrical system in at least oneof: the room, the region, and/or the venue, turning on a securitysystem, turning off the security system, controlling the security systemin at least one of: the room, the region, and/or the venue, turning on amechanical system, turning off a mechanical system, controlling themechanical system in at least one of: the room, the region, and/or thevenue, and/or controlling at least one of: an air conditioning system,heating system, ventilation system, lighting system, heating device,stove, entertainment system, door, fence, window, garage, computersystem, networked device, networked system, home appliance, officeequipment, lighting device, robot (e.g. robotic arm), smart vehicle,smart machine, assembly line, smart device, internet-of-thing (IoT)device, smart home device, and/or a smart office device.

The task may include: detect a user returning home, detect a userleaving home, detect a user moving from one room to another,detect/control/lock/unlock/open/close/partially open awindow/door/garage door/blind/curtain/panel/solar panel/sun shade,detect a pet, detect/monitor a user doing something (e.g. sleeping onsofa, sleeping in bedroom, running on treadmill, cooking, sitting onsofa, watching TV, eating in kitchen, eating in dining room, goingupstairs/downstairs, going outside/coming back, in the rest room),monitor/detect location of a user/pet, do something (e.g. send amessage, notify/report to someone) automatically upon detection, dosomething for the user automatically upon detecting the user, turnon/off/dim a light, turn on/off music/radio/home entertainment system,turn on/off/adjust/control TV/HiFi/set-top-box (STB)/home entertainmentsystem/smart speaker/smart device, turn on/off/adjust air conditioningsystem, turn on/off/adjust ventilation system, turn on/off/adjustheating system, adjust/control curtains/light shades, turn on/off/wake acomputer, turn on/off/pre-heat/control coffee machine/hot water pot,turn on/off/control/preheat cooker/oven/microwave oven/another cookingdevice, check/adjust temperature, check weather forecast, checktelephone message box, check mail, do a system check, control/adjust asystem, check/control/arm/disarm security system/baby monitor,check/control refrigerator, give a report (e.g. through a speaker suchas Google home, Amazon Echo, on a display/screen, via awebpage/email/messaging system/notification system).

For example, when a user arrives home in his car, the task may be to,automatically, detect the user or his car approaching, open the garagedoor upon detection, turn on the driveway/garage light as the userapproaches the garage, turn on air conditioner/heater/fan, etc. As theuser enters the house, the task may be to, automatically, turn on theentrance light, turn off driveway/garage light, play a greeting messageto welcome the user, turn on the music, turn on the radio and tuning tothe user's favorite radio news channel, open the curtain/blind, monitorthe user's mood, adjust the lighting and sound environment according tothe user's mood or the current/imminent event (e.g. do romantic lightingand music because the user is scheduled to eat dinner with girlfriend in1 hour) on the user's daily calendar, warm the food in microwave thatthe user prepared in the morning, do a diagnostic check of all systemsin the house, check weather forecast for tomorrow's work, check news ofinterest to the user, check user's calendar and to-do list and playreminder, check telephone answer system/messaging system/email and givea verbal report using dialog system/speech synthesis, remind (e.g. usingaudible tool such as speakers/HiFi/speechsynthesis/sound/voice/music/song/sound field/background soundfield/dialog system, using visual tool such as TV/entertainmentsystem/computer/notebook/smartpad/display/light/color/brightness/patterns/symbols, using haptictool/virtual reality tool/gesture/tool, using a smartdevice/appliance/material/furniture/fixture, using web tool/server/hubdevice/cloud server/fog server/edge server/home network/mesh network,using messaging tool/notification tool/communication tool/schedulingtool/email, using user interface/GUI, using scent/smell/fragrance/taste,using neural tool/nervous system tool, using a combination) the user ofhis mother's birthday and to call her, prepare a report, and give thereport (e.g. using a tool for reminding as discussed above). The taskmay turn on the air conditioner/heater/ventilation system in advance, oradjust temperature setting of smart thermostat in advance, etc. As theuser moves from the entrance to the living room, the task may be to turnon the living room light, open the living room curtain, open the window,turn off the entrance light behind the user, turn on the TV and set-topbox, set TV to the user's favorite channel, adjust an applianceaccording to the user's preference and conditions/states (e.g. adjustlighting and choose/play music to build a romantic atmosphere), etc.

Another example may be: When the user wakes up in the morning, the taskmay be to detect the user moving around in the bedroom, open theblind/curtain, open the window, turn off the alarm clock, adjust indoortemperature from night-time temperature profile to day-time temperatureprofile, turn on the bedroom light, turn on the restroom light as theuser approaches the restroom, check radio or streaming channel and playmorning news, turn on the coffee machine and preheat the water, turn offsecurity system, etc. When the user walks from bedroom to kitchen, thetask may be to turn on the kitchen and hallway lights, turn off thebedroom and restroom lights, move the music/message/reminder from thebedroom to the kitchen, turn on the kitchen TV, change TV to morningnews channel, lower the kitchen blind and open the kitchen window tobring in fresh air, unlock backdoor for the user to check the backyard,adjust temperature setting for the kitchen, etc. Another example may be:When the user leaves home for work, the task may be to detect the userleaving, play a farewell and/or have-a-good-day message, open/closegarage door, turn on/off garage light and driveway light, turn off/dimlights to save energy (just in case the user forgets), close/lock allwindows/doors (just in case the user forgets), turn off appliance(especially stove, oven, microwave oven), turn on/arm the home securitysystem to guard the home against any intruder, adjust airconditioning/heating/ventilation systems to “away-from-home” profile tosave energy, send alerts/reports/updates to the user's smart phone, etc.

A motion may comprise at least one of: a no-motion, resting motion,non-moving motion, movement, change in position/location, deterministicmotion, transient motion, fall-down motion, repeating motion, periodicmotion, pseudo-periodic motion, periodic/repeated motion associated withbreathing, periodic/repeated motion associated with heartbeat,periodic/repeated motion associated with living object,periodic/repeated motion associated with machine, periodic/repeatedmotion associated with man-made object, periodic/repeated motionassociated with nature, complex motion with transient element andperiodic element, repetitive motion, non-deterministic motion,probabilistic motion, chaotic motion, random motion, complex motion withnon-deterministic element and deterministic element, stationary randommotion, pseudo-stationary random motion, cyclo-stationary random motion,non-stationary random motion, stationary random motion with periodicautocorrelation function (ACF), random motion with periodic ACF forperiod of time, random motion that is pseudo-stationary for a period oftime, random motion of which an instantaneous ACF has apseudo-periodic/repeating element for a period of time, machine motion,mechanical motion, vehicle motion, drone motion, air-related motion,wind-related motion, weather-related motion, water-related motion,fluid-related motion, ground-related motion, change in electro-magneticcharacteristics, sub-surface motion, seismic motion, plant motion,animal motion, human motion, normal motion, abnormal motion, dangerousmotion, warning motion, suspicious motion, rain, fire, flood, tsunami,explosion, collision, imminent collision, human body motion, headmotion, facial motion, eye motion, mouth motion, tongue motion, neckmotion, finger motion, hand motion, arm motion, shoulder motion, bodymotion, chest motion, abdominal motion, hip motion, leg motion, footmotion, body joint motion, knee motion, elbow motion, upper body motion,lower body motion, skin motion, below-skin motion, subcutaneous tissuemotion, blood vessel motion, intravenous motion, organ motion, heartmotion, lung motion, stomach motion, intestine motion, bowel motion,eating motion, breathing motion, facial expression, eye expression,mouth expression, talking motion, singing motion, eating motion,gesture, hand gesture, arm gesture, keystroke, typing stroke,user-interface gesture, man-machine interaction, gait, dancing movement,coordinated movement, and/or coordinated body movement.

The heterogeneous IC of the Type 1 device and/or any Type 2 receiver maycomprise low-noise amplifier (LNA), power amplifier, transmit-receiveswitch, media access controller, baseband radio, 2.4 GHz radio, 3.65 GHzradio, 4.9 GHz radio, 5 GHz radio, 5.9 GHz radio, below 6 GHz radio,below 60 GHz radio and/or another radio. The heterogeneous IC maycomprise a processor, a memory communicatively coupled with theprocessor, and a set of instructions stored in the memory to be executedby the processor. The IC and/or any processor may comprise at least oneof: general purpose processor, special purpose processor,microprocessor, multi-processor, multi-core processor, parallelprocessor, CISC processor, RISC processor, microcontroller, centralprocessing unit (CPU), graphical processor unit (GPU), digital signalprocessor (DSP), application specific integrated circuit (ASIC), fieldprogrammable gate array (FPGA), embedded processor (e.g. ARM), logiccircuit, other programmable logic device, discrete logic, and/or acombination. The heterogeneous IC may support broadband network,wireless network, mobile network, mesh network, cellular network,wireless local area network (WLAN), wide area network (WAN), andmetropolitan area network (MAN), WLAN standard, WiFi, LTE, LTE-A, LTE-U,802.11 standard, 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11ad,802.11af, 802,11ah, 802.11ax, 802.11ay, mesh network standard, 802.15standard, 802.16 standard, cellular network standard, 3G, 3.5G, 4G,beyond 4G, 4.5G, 5G, 6G, 7G, 8G, 9G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA,CDMA, WCDMA, TD-SCDMA, Bluetooth, Bluetooth Low-Energy (BLE), NFC,Zigbee, WiMax, and/or another wireless network protocol.

The processor may comprise general purpose processor, special purposeprocessor, microprocessor, microcontroller, embedded processor, digitalsignal processor, central processing unit (CPU), graphical processingunit (GPU), multi-processor, multi-core processor, and/or processor withgraphics capability, and/or a combination. The memory may be volatile,non-volatile, random access memory (RAM), Read Only Memory (ROM),Electrically Programmable ROM (EPROM), Electrically ErasableProgrammable ROM (EEPROM), hard disk, flash memory, CD-ROM, DVD-ROM,magnetic storage, optical storage, organic storage, storage system,storage network, network storage, cloud storage, edge storage, localstorage, external storage, internal storage, or other form ofnon-transitory storage medium known in the art. The set of instructions(machine executable code) corresponding to the method steps may beembodied directly in hardware, in software, in firmware, or incombinations thereof. The set of instructions may be embedded,pre-loaded, loaded upon boot up, loaded on the fly, loaded on demand,pre-installed, installed, and/or downloaded.

The presentation may be a presentation in an audio-visual way (e.g.using combination of visual, graphics, text, symbols, color, shades,video, animation, sound, speech, audio, etc.), graphical way (e.g. usingGUI, animation, video), textual way (e.g. webpage with text, message,animated text), symbolic way (e.g. emoticon, signs, hand gesture), ormechanical way (e.g. vibration, actuator movement, haptics, etc.).

Basic Computation

Computational workload associated with the method is shared among theprocessor, the Type 1 heterogeneous wireless device, the Type 2heterogeneous wireless device, a local server (e.g. hub device), a cloudserver, and another processor.

An operation, pre-processing, processing and/or postprocessing may beapplied to data (e.g. TSCI, autocorrelation, features of TSCI). Anoperation may be preprocessing, processing and/or postprocessing. Thepreprocessing, processing and/or postprocessing may be an operation. Anoperation may comprise preprocessing, processing, post-processing,scaling, computing a confidence factor, computing a line-of-sight (LOS)quantity, computing a non-LOS (NLOS) quantity, a quantity comprising LOSand NLOS, computing a single link (e.g. path, communication path, linkbetween a transmitting antenna and a receiving antenna) quantity,computing a quantity comprising multiple links, computing a function ofthe operands, filtering, linear filtering, nonlinear filtering, folding,grouping, energy computation, lowpass filtering, bandpass filtering,highpass filtering, median filtering, rank filtering, quartilefiltering, percentile filtering, mode filtering, finite impulse response(FIR) filtering, infinite impulse response (IIR) filtering, movingaverage (MA) filtering, autoregressive (AR) filtering, autoregressivemoving averaging (ARMA) filtering, selective filtering, adaptivefiltering, interpolation, decimation, subsampling, upsampling,resampling, time correction, time base correction, phase correction,magnitude correction, phase cleaning, magnitude cleaning, matchedfiltering, enhancement, restoration, denoising, smoothing, signalconditioning, enhancement, restoration, spectral analysis, lineartransform, nonlinear transform, inverse transform, frequency transform,inverse frequency transform, Fourier transform (FT), discrete time FT(DTFT), discrete FT (DFT), fast FT (FFT), wavelet transform, Laplacetransform, Hilbert transform, Hadamard transform, trigonometrictransform, sine transform, cosine transform, DCT, power-of-2 transform,sparse transform, graph-based transform, graph signal processing, fasttransform, a transform combined with zero padding, cyclic padding,padding, zero padding, feature extraction, decomposition, projection,orthogonal projection, non-orthogonal projection, over-completeprojection, eigen-decomposition, singular value decomposition (SVD),principle component analysis (PCA), independent component analysis(ICA), grouping, sorting, thresholding, soft thresholding, hardthresholding, clipping, soft clipping, first derivative, second orderderivative, high order derivative, convolution, multiplication,division, addition, subtraction, integration, maximization,minimization, least mean square error, recursive least square,constrained least square, batch least square, least absolute error,least mean square deviation, least absolute deviation, localmaximization, local minimization, optimization of a cost function,neural network, recognition, labeling, training, clustering, machinelearning, supervised learning, unsupervised learning, semi-supervisedlearning, comparison with another TSCI, similarity score computation,quantization, vector quantization, matching pursuit, compression,encryption, coding, storing, transmitting, normalization, temporalnormalization, frequency domain normalization, classification,clustering, labeling, tagging, learning, detection, estimation, learningnetwork, mapping, remapping, expansion, storing, retrieving,transmitting, receiving, representing, merging, combining, splitting,tracking, monitoring, matched filtering, Kalman filtering, particlefilter, intrapolation, extrapolation, histogram estimation, importancesampling, Monte Carlo sampling, compressive sensing, representing,merging, combining, splitting, scrambling, error protection, forwarderror correction, doing nothing, time varying processing, conditioningaveraging, weighted averaging, arithmetic mean, geometric mean, harmonicmean, averaging over selected frequency, averaging over antenna links,logical operation, permutation, combination, sorting, AND, OR, XOR,union, intersection, vector addition, vector subtraction, vectormultiplication, vector division, inverse, norm, distance, and/or anotheroperation. The operation may be the preprocessing, processing, and/orpost-processing. Operations may be applied jointly on multiple timeseries or functions.

The function (e.g. function of operands) may comprise: scalar function,vector function, discrete function, continuous function, polynomialfunction, characteristics, feature, magnitude, phase, exponentialfunction, logarithmic function, trigonometric function, transcendentalfunction, logical function, linear function, algebraic function,nonlinear function, piecewise linear function, real function, complexfunction, vector-valued function, inverse function, derivative offunction, integration of function, circular function, function ofanother function, one-to-one function, one-to-many function, many-to-onefunction, many-to-many function, zero crossing, absolute function,indicator function, mean, mode, median, range, statistics, histogram,variance, standard deviation, measure of variation, spread, dispersion,deviation, divergence, range, interquartile range, total variation,absolute deviation, total deviation, arithmetic mean, geometric mean,harmonic mean, trimmed mean, percentile, square, cube, root, power,sine, cosine, tangent, cotangent, secant, cosecant, elliptical function,parabolic function, hyperbolic function, game function, zeta function,absolute value, thresholding, limiting function, floor function,rounding function, sign function, quantization, piecewise constantfunction, composite function, function of function, time functionprocessed with an operation (e.g. filtering), probabilistic function,stochastic function, random function, ergodic function, stationaryfunction, deterministic function, periodic function, repeated function,transformation, frequency transform, inverse frequency transform,discrete time transform, Laplace transform, Hilbert transform, sinetransform, cosine transform, triangular transform, wavelet transform,integer transform, power-of-2 transform, sparse transform, projection,decomposition, principle component analysis (PCA), independent componentanalysis (ICA), neural network, feature extraction, moving function,function of moving window of neighboring items of time series, filteringfunction, convolution, mean function, histogram, variance/standarddeviation function, statistical function, short-time transform, discretetransform, discrete Fourier transform, discrete cosine transform,discrete sine transform, Hadamard transform, eigen-decomposition,eigenvalue, singular value decomposition (SVD), singular value,orthogonal decomposition, matching pursuit, sparse transform, sparseapproximation, any decomposition, graph-based processing, graph-basedtransform, graph signal processing, classification, identifying aclass/group/category, labeling, learning, machine learning, detection,estimation, feature extraction, learning network, feature extraction,denoising, signal enhancement, coding, encryption, mapping, remapping,vector quantization, lowpass filtering, highpass filtering, bandpassfiltering, matched filtering, Kalman filtering, preprocessing,postprocessing, particle filter, FIR filtering, IIR filtering,autoregressive (AR) filtering, adaptive filtering, first orderderivative, high order derivative, integration, zero crossing,smoothing, median filtering, mode filtering, sampling, random sampling,resampling function, downsampling, down-converting, upsampling,up-converting, interpolation, extrapolation, importance sampling, MonteCarlo sampling, compressive sensing, statistics, short term statistics,long term statistics, autocorrelation function, cross correlation,moment generating function, time averaging, weighted averaging, specialfunction, Bessel function, error function, complementary error function,Beta function, Gamma function, integral function, Gaussian function,Poisson function, etc.

Machine learning, training, discriminative training, deep learning,neural network, continuous time processing, distributed computing,distributed storage, acceleration usingGPU/DSP/coprocessor/multicore/multiprocessing may be applied to a step(or each step) of this disclosure.

A frequency transform may include Fourier transform, Laplace transform,Hadamard transform, Hilbert transform, sine transform, cosine transform,triangular transform, wavelet transform, integer transform, power-of-2transform, combined zero padding and transform, Fourier transform withzero padding, and/or another transform. Fast versions and/orapproximated versions of the transform may be performed. The transformmay be performed using floating point, and/or fixed point arithmetic.

An inverse frequency transform may include inverse Fourier transform,inverse Laplace transform, inverse Hadamard transform, inverse Hilberttransform, inverse sine transform, inverse cosine transform, inversetriangular transform, inverse wavelet transform, inverse integertransform, inverse power-of-2 transform, combined zero padding andtransform, inverse Fourier transform with zero padding, and/or anothertransform. Fast versions and/or approximated versions of the transformmay be performed. The transform may be performed using floating point,and/or fixed point arithmetic.

A quantity/feature from a TSCI may be computed. The quantity maycomprise statistic of at least one of: motion, location, map coordinate,height, speed, acceleration, movement angle, rotation, size, volume,time trend, pattern, one-time pattern, repeating pattern, evolvingpattern, time pattern, mutually excluding patterns, related/correlatedpatterns, cause-and-effect, correlation, short-term/long-termcorrelation, tendency, inclination, statistics, typical behavior,atypical behavior, time trend, time profile, periodic motion, repeatedmotion, repetition, tendency, change, abrupt change, gradual change,frequency, transient, breathing, gait, action, event, suspicious event,dangerous event, alarming event, warning, belief, proximity, collision,power, signal, signal power, signal strength, signal intensity, receivedsignal strength indicator (RSSI), signal amplitude, signal phase, signalfrequency component, signal frequency band component, channel stateinformation (CSI), map, time, frequency, time-frequency, decomposition,orthogonal decomposition, non-orthogonal decomposition, tracking,breathing, heart beat, statistical parameters, cardiopulmonarystatistics/analytics (e.g. output responses), daily activitystatistics/analytics, chronic disease statistics/analytics, medicalstatistics/analytics, an early (or instantaneous or contemporaneous ordelayed) indication/suggestion/sign/indicator/verifier/detection/symptomof a disease/condition/situation, biometric, baby, patient, machine,device, temperature, vehicle, parking lot, venue, lift, elevator,spatial, road, fluid flow, home, room, office, house, building,warehouse, storage, system, ventilation, fan, pipe, duct, people, human,car, boat, truck, airplane, drone, downtown, crowd, impulsive event,cyclo-stationary, environment, vibration, material, surface,3-dimensional, 2-dimensional, local, global, presence, and/or anothermeasurable quantity/variable.

Sliding Window/Algorithm

Sliding time window may have time varying window width. It may besmaller at the beginning to enable fast acquisition and may increaseover time to a steady-state size. The steady-state size may be relatedto the frequency, repeated motion, transient motion, and/or STI to bemonitored. Even in steady state, the window size may be adaptively(and/or dynamically) changed (e.g. adjusted, varied, modified) based onbattery life, power consumption, available computing power, change inamount of targets, the nature of motion to be monitored, etc.

The time shift between two sliding time windows at adjacent timeinstance may be constant/variable/locally adaptive/dynamically adjustedover time. When shorter time shift is used, the update of any monitoringmay be more frequent which may be used for fast changing situations,object motions, and/or objects. Longer time shift may be used for slowersituations, object motions, and/or objects.

The window width/size and/or time shift may be changed (e.g. adjusted,varied, modified) upon a user request/choice. The time shift may bechanged automatically (e.g. as controlled byprocessor/computer/server/hub device/cloud server) and/or adaptively(and/or dynamically).

At least one characteristics (e.g. characteristic value, orcharacteristic point) of a function (e.g. auto-correlation function,auto-covariance function, cross-correlation function, cross-covariancefunction, power spectral density, time function, frequency domainfunction, frequency transform) may be determined (e.g. by an objecttracking server, the processor, the Type 1 heterogeneous device, theType 2 heterogeneous device, and/or another device). The at least onecharacteristics of the function may include: a maximum, minimum,extremum, local maximum, local minimum, local extremum, local extremumwith positive time offset, first local extremum with positive timeoffset, n{circumflex over ( )}th local extremum with positive timeoffset, local extremum with negative time offset, first local extremumwith negative time offset, n{circumflex over ( )}th local extremum withnegative time offset, constrained maximum, constrained minimum,constrained extremum, significant maximum, significant minimum,significant extremum, slope, derivative, higher order derivative,maximum slope, minimum slope, local maximum slope, local maximum slopewith positive time offset, local minimum slope, constrained maximumslope, constrained minimum slope, maximum higher order derivative,minimum higher order derivative, constrained higher order derivative,zero-crossing, zero crossing with positive time offset, n{circumflexover ( )}th zero crossing with positive time offset, zero crossing withnegative time offset, n{circumflex over ( )}th zero crossing withnegative time offset, constrained zero-crossing, zero-crossing of slope,zero-crossing of higher order derivative, and/or anothercharacteristics. At least one argument of the function associated withthe at least one characteristics of the function may be identified. Somequantity (e.g. spatial-temporal information of the object) may bedetermined based on the at least one argument of the function.

A characteristics (e.g. characteristics of motion of an object in thevenue) may comprise at least one of: an instantaneous characteristics,short-term characteristics, repetitive characteristics, recurringcharacteristics, history, incremental characteristics, changingcharacteristics, deviational characteristics, phase, magnitude, degree,time characteristics, frequency characteristics, time-frequencycharacteristics, decomposition characteristics, orthogonal decompositioncharacteristics, non-orthogonal decomposition characteristics,deterministic characteristics, probabilistic characteristics, stochasticcharacteristics, autocorrelation function (ACF), mean, variance,standard deviation, measure of variation, spread, dispersion, deviation,divergence, range, interquartile range, total variation, absolutedeviation, total deviation, statistics, duration, timing, trend,periodic characteristics, repetition characteristics, long-termcharacteristics, historical characteristics, average characteristics,current characteristics, past characteristics, future characteristics,predicted characteristics, location, distance, height, speed, direction,velocity, acceleration, change of the acceleration, angle, angularspeed, angular velocity, angular acceleration of the object, change ofthe angular acceleration, orientation of the object, angular ofrotation, deformation of the object, shape of the object, change ofshape of the object, change of size of the object, change of structureof the object, and/or change of characteristics of the object.

At least one local maximum and at least one local minimum of thefunction may be identified. At least one localsignal-to-noise-ratio-like (SNR-like) parameter may be computed for eachpair of adjacent local maximum and local minimum. The SNR-like parametermay be a function (e.g. linear, log, exponential function, monotonicfunction) of a fraction of a quantity (e.g. power, magnitude) of thelocal maximum over the same quantity of the local minimum. It may alsobe the function of a difference between the quantity of the localmaximum and the same quantity of the local minimum. Significant localpeaks may be identified or selected. Each significant local peak may bea local maximum with SNR-like parameter greater than a threshold T1and/or a local maximum with amplitude greater than a threshold T2. Theat least one local minimum and the at least one local minimum in thefrequency domain may be identified/computed using a persistence-basedapproach.

A set of selected significant local peaks may be selected from the setof identified significant local peaks based on a selection criterion(e.g. a quality criterion, a signal quality condition). Thecharacteristics/STI of the object may be computed based on the set ofselected significant local peaks and frequency values associated withthe set of selected significant local peaks. In one example, theselection criterion may always correspond to select the strongest peaksin a range. While the strongest peaks may be selected, the unselectedpeaks may still be significant (rather strong).

Unselected significant peaks may be stored and/or monitored as“reserved” peaks for use in future selection in future sliding timewindows. As an example, there may be a particular peak (at a particularfrequency) appearing consistently over time. Initially, it may besignificant but not selected (as other peaks may be stronger). But inlater time, the peak may become stronger and more dominant and may beselected. When it became “selected”, it may be back-traced in time andmade “selected” in the earlier time when it was significant but notselected. In such case, the back-traced peak may replace a previouslyselected peak in an early time. The replaced peak may be the relativelyweakest, or a peak that appear in isolation in time (i.e. appearing onlybriefly in time).

In another example, the selection criterion may not correspond to selectthe strongest peaks in the range. Instead, it may consider not only the“strength” of the peak, but the “trace” of the peak-peaks that may havehappened in the past, especially those peaks that have been identifiedfor a long time.

For example, if a finite state machine (FSM) is used, it may select thepeak(s) based on the state of the FSM. Decision thresholds may becomputed adaptively (and/or dynamically) based on the state of the FSM.

A similarity score and/or component similarity score may be computed(e.g. by a server (e.g. hub device), the processor, the Type 1 device,the Type 2 device, a local server, a cloud server, and/or anotherdevice) based on a pair of temporally adjacent CI of a TSCI. The pairmay come from the same sliding window or two different sliding windows.The similarity score may also be based on a pair of, temporally adjacentor not so adjacent, CI from two different TSCI. The similarity scoreand/or component similar score may be/comprise: time reversal resonatingstrength (TRRS), correlation, cross-correlation, auto-correlation,correlation indicator, covariance, cross-covariance, auto-covariance,inner product of two vectors, distance score, norm, metric, qualitymetric, signal quality condition, statistical characteristics,discrimination score, neural network, deep learning network, machinelearning, training, discrimination, weighted averaging, preprocessing,denoising, signal conditioning, filtering, time correction, timingcompensation, phase offset compensation, transformation, component-wiseoperation, feature extraction, finite state machine, and/or anotherscore. The characteristics and/or STI may be determined/computed basedon the similarity score.

Any threshold may be pre-determined, adaptively (and/or dynamically)determined and/or determined by a finite state machine. The adaptivedetermination may be based on time, space, location, antenna, path,link, state, battery life, remaining battery life, available power,available computational resources, available network bandwidth, etc.

A threshold to be applied to a test statistics to differentiate twoevents (or two conditions, or two situations, or two states), A and B,may be determined. Data (e.g. CI, channel state information (CSI), powerparameter) may be collected under A and/or under B in a trainingsituation. The test statistics may be computed based on the data.Distributions of the test statistics under A may be compared withdistributions of the test statistics under B (reference distribution),and the threshold may be chosen according to some criteria. The criteriamay comprise: maximum likelihood (ML), maximum aposterior probability(MAP), discriminative training, minimum Type 1 error for a given Type 2error, minimum Type 2 error for a given Type 1 error, and/or othercriteria (e.g. a quality criterion, signal quality condition). Thethreshold may be adjusted to achieve different sensitivity to the A, Band/or another event/condition/situation/state. The threshold adjustmentmay be automatic, semi-automatic and/or manual. The threshold adjustmentmay be applied once, sometimes, often, periodically, repeatedly,occasionally, sporadically, and/or on demand. The threshold adjustmentmay be adaptive (and/or dynamically adjusted). The threshold adjustmentmay depend on the object, object movement/location/direction/action,object characteristics/STI/size/property/trait/habit/behavior, thevenue, feature/fixture/furniture/barrier/material/machine/livingthing/thing/object/boundary/surface/medium that is in/at/of the venue,map, constraint of the map (or environmental model), theevent/state/situation/condition, time, timing, duration, current state,past history, user, and/or a personal preference, etc.

A stopping criterion (or skipping or bypassing or blocking or pausing orpassing or rejecting criterion) of an iterative algorithm may be thatchange of a current parameter (e.g. offset value) in the updating in aniteration is less than a threshold. The threshold may be 0.5, 1, 1.5, 2,or another number. The threshold may be adaptive (and/or dynamicallyadjusted). It may change as the iteration progresses. For the offsetvalue, the adaptive threshold may be determined based on the task,particular value of the first time, the current time offset value, theregression window, the regression analysis, the regression function, theregression error, the convexity of the regression function, and/or aniteration number.

The local extremum may be determined as the corresponding extremum ofthe regression function in the regression window. The local extremum maybe determined based on a set of time offset values in the regressionwindow and a set of associated regression function values. Each of theset of associated regression function values associated with the set oftime offset values may be within a range from the corresponding extremumof the regression function in the regression window.

The searching for a local extremum may comprise robust search,minimization, maximization, optimization, statistical optimization, dualoptimization, constraint optimization, convex optimization, globaloptimization, local optimization an energy minimization, linearregression, quadratic regression, higher order regression, linearprogramming, nonlinear programming, stochastic programming,combinatorial optimization, constraint programming, constraintsatisfaction, calculus of variations, optimal control, dynamicprogramming, mathematical programming, multi-objective optimization,multi-modal optimization, disjunctive programming, space mapping,infinite-dimensional optimization, heuristics, metaheuristics, convexprogramming, semidefinite programming, conic programming, coneprogramming, integer programming, quadratic programming, fractionalprogramming, numerical analysis, simplex algorithm, iterative method,gradient descent, subgradient method, coordinate descent, conjugategradient method, Newton's algorithm, sequential quadratic programming,interior point method, ellipsoid method, reduced gradient method,quasi-Newton method, simultaneous perturbation stochastic approximation,interpolation method, pattern search method, line search,non-differentiable optimization, genetic algorithm, evolutionaryalgorithm, dynamic relaxation, hill climbing, particle swarmoptimization, gravitation search algorithm, simulated annealing, memeticalgorithm, differential evolution, dynamic relaxation, stochastictunneling, Tabu search, reactive search optimization, curve fitting,least square, simulation based optimization, variational calculus,and/or variant. The search for local extremum may be associated with anobjective function, loss function, cost function, utility function,fitness function, energy function, and/or an energy function.

Regression may be performed using regression function to fit sampleddata (e.g. CI, feature of CI, component of CI) or another function (e.g.autocorrelation function) in a regression window. In at least oneiteration, a length of the regression window and/or a location of theregression window may change. The regression function may be linearfunction, quadratic function, cubic function, polynomial function,and/or another function.

The regression analysis may minimize at least one of: error, aggregateerror, component error, error in projection domain, error in selectedaxes, error in selected orthogonal axes, absolute error, square error,absolute deviation, square deviation, higher order error (e.g. thirdorder, fourth order), robust error (e.g. square error for smaller errormagnitude and absolute error for larger error magnitude, or first kindof error for smaller error magnitude and second kind of error for largererror magnitude), another error, weighted sum (or weighted mean) ofabsolute/square error (e.g. for wireless transmitter with multipleantennas and wireless receiver with multiple antennas, each pair oftransmitter antenna and receiver antenna form a link), mean absoluteerror, mean square error, mean absolute deviation, and/or mean squaredeviation. Error associated with different links may have differentweights. One possibility is that some links and/or some components withlarger noise or lower signal quality metric may have smaller or biggerweight.), weighted sum of square error, weighted sum of higher ordererror, weighted sum of robust error, weighted sum of the another error,absolute cost, square cost, higher order cost, robust cost, anothercost, weighted sum of absolute cost, weighted sum of square cost,weighted sum of higher order cost, weighted sum of robust cost, and/orweighted sum of another cost.

The regression error determined may be an absolute error, square error,higher order error, robust error, yet another error, weighted sum ofabsolute error, weighted sum of square error, weighted sum of higherorder error, weighted sum of robust error, and/or weighted sum of theyet another error.

The time offset associated with maximum regression error (or minimumregression error) of the regression function with respect to theparticular function in the regression window may become the updatedcurrent time offset in the iteration.

A local extremum may be searched based on a quantity comprising adifference of two different errors (e.g. a difference between absoluteerror and square error). Each of the two different errors may comprisean absolute error, square error, higher order error, robust error,another error, weighted sum of absolute error, weighted sum of squareerror, weighted sum of higher order error, weighted sum of robust error,and/or weighted sum of the another error.

The quantity may be compared with a reference data or a referencedistribution, such as an F-distribution, central F-distribution, anotherstatistical distribution, threshold, threshold associated withprobability/histogram, threshold associated with probability/histogramof finding false peak, threshold associated with the F-distribution,threshold associated the central F-distribution, and/or thresholdassociated with the another statistical distribution.

The regression window may be determined based on at least one of: themovement (e.g. change in position/location) of the object, quantityassociated with the object, the at least one characteristics and/or STIof the object associated with the movement of the object, estimatedlocation of the local extremum, noise characteristics, estimated noisecharacteristics, signal quality metric, F-distribution, centralF-distribution, another statistical distribution, threshold, presetthreshold, threshold associated with probability/histogram, thresholdassociated with desired probability, threshold associated withprobability of finding false peak, threshold associated with theF-distribution, threshold associated the central F-distribution,threshold associated with the another statistical distribution,condition that quantity at the window center is largest within theregression window, condition that the quantity at the window center islargest within the regression window, condition that there is only oneof the local extremum of the particular function for the particularvalue of the first time in the regression window, another regressionwindow, and/or another condition.

The width of the regression window may be determined based on theparticular local extremum to be searched. The local extremum maycomprise first local maximum, second local maximum, higher order localmaximum, first local maximum with positive time offset value, secondlocal maximum with positive time offset value, higher local maximum withpositive time offset value, first local maximum with negative timeoffset value, second local maximum with negative time offset value,higher local maximum with negative time offset value, first localminimum, second local minimum, higher local minimum, first local minimumwith positive time offset value, second local minimum with positive timeoffset value, higher local minimum with positive time offset value,first local minimum with negative time offset value, second localminimum with negative time offset value, higher local minimum withnegative time offset value, first local extremum, second local extremum,higher local extremum, first local extremum with positive time offsetvalue, second local extremum with positive time offset value, higherlocal extremum with positive time offset value, first local extremumwith negative time offset value, second local extremum with negativetime offset value, and/or higher local extremum with negative timeoffset value.

A current parameter (e.g. time offset value) may be initialized based ona target value, target profile, trend, past trend, current trend, targetspeed, speed profile, target speed profile, past speed trend, the motionor movement (e.g. change in position/location) of the object, at leastone characteristics and/or STI of the object associated with themovement of object, positional quantity of the object, initial speed ofthe object associated with the movement of the object, predefined value,initial width of the regression window, time duration, value based oncarrier frequency of the signal, value based on subcarrier frequency ofthe signal, bandwidth of the signal, amount of antennas associated withthe channel, noise characteristics, signal h metric, and/or an adaptive(and/or dynamically adjusted) value. The current time offset may be atthe center, on the left side, on the right side, and/or at another fixedrelative location, of the regression window.

In the presentation, information may be displayed with a map (orenvironmental model) of the venue. The information may comprise:location, zone, region, area, coverage area, corrected location,approximate location, location with respect to (w.r.t.) a map of thevenue, location w.r.t. a segmentation of the venue, direction, path,path w.r.t. the map and/or the segmentation, trace (e.g. location withina time window such as the past 5 seconds, or past 10 seconds; the timewindow duration may be adjusted adaptively (and/or dynamically); thetime window duration may be adaptively (and/or dynamically) adjustedw.r.t. speed, acceleration, etc.), history of a path, approximateregions/zones along a path, history/summary of past locations, historyof past locations of interest, frequently-visited areas, customertraffic, crowd distribution, crowd behavior, crowd control information,speed, acceleration, motion statistics, breathing rate, heart rate,presence/absence of motion, presence/absence of people or pets orobject, presence/absence of vital sign, gesture, gesture control(control of devices using gesture), location-based gesture control,information of a location-based operation, identity (ID) or identifierof the respect object (e.g. pet, person, self-guided machine/device,vehicle, drone, car, boat, bicycle, self-guided vehicle, machine withfan, air-conditioner, TV, machine with movable part), identification ofa user (e.g. person), information of the user,location/speed/acceleration/direction/motion/gesture/gesturecontrol/motion trace of the user, ID or identifier of the user, activityof the user, state of the user, sleeping/resting characteristics of theuser, emotional state of the user, vital sign of the user, environmentinformation of the venue, weather information of the venue, earthquake,explosion, storm, rain, fire, temperature, collision, impact, vibration,event, door-open event, door-close event, window-open event,window-close event, fall-down event, burning event, freezing event,water-related event, wind-related event, air-movement event, accidentevent, pseudo-periodic event (e.g. running on treadmill, jumping up anddown, skipping rope, somersault, etc.), repeated event, crowd event,vehicle event, gesture of the user (e.g. hand gesture, arm gesture, footgesture, leg gesture, body gesture, head gesture, face gesture, mouthgesture, eye gesture, etc.).

The location may be 2-dimensional (e.g. with 2D coordinates),3-dimensional (e.g. with 3D coordinates). The location may be relative(e.g. w.r.t. a map or environmental model) or relational (e.g. halfwaybetween point A and point B, around a corner, up the stairs, on top oftable, at the ceiling, on the floor, on a sofa, close to point A, adistance R from point A, within a radius of R from point A, etc.). Thelocation may be expressed in rectangular coordinate, polar coordinate,and/or another representation.

The information (e.g. location) may be marked with at least one symbol.The symbol may be time varying. The symbol may be flashing and/orpulsating with or without changing color/intensity. The size may changeover time. The orientation of the symbol may change over time. Thesymbol may be a number that reflects an instantaneous quantity (e.g.vital sign/breathing rate/heart rate/gesture/state/status/action/motionof a user, temperature, network traffic, network connectivity, status ofa device/machine, remaining power of a device, status of the device,etc.). The rate of change, the size, the orientation, the color, theintensity and/or the symbol may reflect the respective motion. Theinformation may be presented visually and/or described verbally (e.g.using pre-recorded voice, or voice synthesis). The information may bedescribed in text. The information may also be presented in a mechanicalway (e.g. an animated gadget, a movement of a movable part).

The user-interface (UI) device may be a smart phone (e.g. iPhone,Android phone), tablet (e.g. iPad), laptop (e.g. notebook computer),personal computer (PC), device with graphical user interface (GUI),smart speaker, device with voice/audio/speaker capability, virtualreality (VR) device, augmented reality (AR) device, smart car, displayin the car, voice assistant, voice assistant in a car, etc.

The map (or environmental model) may be 2-dimensional, 3-dimensionaland/or higher-dimensional. (e.g. a time varying 2D/3D map/environmentalmodel) Walls, windows, doors, entrances, exits, forbidden areas may bemarked on the map or the model. The map may comprise floor plan of afacility. The map or model may have one or more layers (overlays). Themap/model may be a maintenance map/model comprising water pipes, gaspipes, wiring, cabling, air ducts, crawl-space, ceiling layout, and/orunderground layout. The venue may be segmented/subdivided/zoned/groupedinto multiple zones/regions/geographicregions/sectors/sections/territories/districts/precincts/localities/neighborhoods/areas/stretches/expansesuch as bedroom, living room, storage room, walkway, kitchen, diningroom, foyer, garage, first floor, second floor, rest room, offices,conference room, reception area, various office areas, various warehouseregions, various facility areas, etc. The segments/regions/areas may bepresented in a map/model. Different regions may be color-coded.Different regions may be presented with a characteristic (e.g. color,brightness, color intensity, texture, animation, flashing, flashingrate, etc.). Logical segmentation of the venue may be done using the atleast one heterogeneous Type 2 device, or a server (e.g. hub device), ora cloud server, etc.

Here is an example of the disclosed system, apparatus, and method.Stephen and his family want to install the disclosed wireless motiondetection system to detect motion in their 2000 sqft two-storey townhouse in Seattle, Wash. Because his house has two storeys, Stephendecided to use one Type 2 device (named A) and two Type 1 devices (namedB and C) in the ground floor. His ground floor has predominantly threerooms: kitchen, dining room and living room arranged in a straight line,with the dining room in the middle. The kitchen and the living rooms areon opposite end of the house. He put the Type 2 device (A) in the diningroom, and put one Type 1 device (B) in the kitchen and the other Type 1device (C) in the living room. With this placement of the devices, he ispractically partitioning the ground floor into 3 zones (dining room,living room and kitchen) using the motion detection system. When motionis detected by the AB pair and the AC pair, the system would analyze themotion information and associate the motion with one of the 3 zones.

When Stephen and his family go out on weekends (e.g. to go for a campduring a long weekend), Stephen would use a mobile phone app (e.g.Android phone app or iPhone app) to turn on the motion detection system.When the system detects motion, a warning signal is sent to Stephen(e.g. an SMS text message, an email, a push message to the mobile phoneapp, etc.). If Stephen pays a monthly fee (e.g. $10/month), a servicecompany (e.g. security company) will receive the warning signal throughwired network (e.g. broadband) or wireless network (e.g. home WiFi, LTE,3G, 2.5G, etc.) and perform a security procedure for Stephen (e.g. callhim to verify any problem, send someone to check on the house, contactthe police on behalf of Stephen, etc.). Stephen loves his aging motherand cares about her well-being when she is alone in the house. When themother is alone in the house while the rest of the family is out (e.g.go to work, or shopping, or go on vacation), Stephen would turn on themotion detection system using his mobile app to ensure the mother is ok.He then uses the mobile app to monitor his mother's movement in thehouse. When Stephen uses the mobile app to see that the mother is movingaround the house among the 3 regions, according to her daily routine,Stephen knows that his mother is doing ok. Stephen is thankful that themotion detection system can help him monitor his mother's well-beingwhile he is away from the house.

On a typical day, the mother would wake up at around 7 AM. She wouldcook her breakfast in the kitchen for about 20 minutes. Then she wouldeat the breakfast in the dining room for about 30 minutes. Then shewould do her daily exercise in the living room, before sitting down onthe sofa in the living room to watch her favorite TV show. The motiondetection system enables Stephen to see the timing of the movement ineach of the 3 regions of the house. When the motion agrees with thedaily routine, Stephen knows roughly that the mother should be doingfine. But when the motion pattern appears abnormal (e.g. there is nomotion until 10 AM, or she stayed in the kitchen for too long, or sheremains motionless for too long, etc.), Stephen suspects something iswrong and would call the mother to check on her. Stephen may even getsomeone (e.g. a family member, a neighbor, a paid personnel, a friend, asocial worker, a service provider) to check on his mother.

At some time, Stephen feels like repositioning the Type 2 device. Hesimply unplugs the device from the original AC power plug and plug itinto another AC power plug. He is happy that the wireless motiondetection system is plug-and-play and the repositioning does not affectthe operation of the system. Upon powering up, it works right away.

Sometime later, Stephen is convinced that our wireless motion detectionsystem can really detect motion with very high accuracy and very lowalarm, and he really can use the mobile app to monitor the motion in theground floor. He decides to install a similar setup (i.e. one Type 2device and two Type 1 devices) in the second floor to monitor thebedrooms in the second floor. Once again, he finds that the system setup is extremely easy as he simply needs to plug the Type 2 device andthe Type 1 devices into the AC power plug in the second floor. Nospecial installation is needed. And he can use the same mobile app tomonitor motion in the ground floor and the second floor. Each Type 2device in the ground floor/second floor can interact with all the Type 1devices in both the ground floor and the second floor. Stephen is happyto see that, as he doubles his investment in the Type 1 and Type 2devices, he has more than double the capability of the combined systems.

According to various embodiments, each CI (CI) may comprise at least oneof: channel state information (CSI), frequency domain CSI, frequencyrepresentation of CSI, frequency domain CSI associated with at least onesub-band, time domain CSI, CSI in domain, channel response, estimatedchannel response, channel impulse response (CIR), channel frequencyresponse (CFR), channel characteristics, channel filter response, CSI ofthe wireless multipath channel, information of the wireless multipathchannel, timestamp, auxiliary information, data, meta data, user data,account data, access data, security data, session data, status data,supervisory data, household data, identity (ID), identifier, devicedata, network data, neighborhood data, environment data, real-time data,sensor data, stored data, encrypted data, compressed data, protecteddata, and/or another CI. In one embodiment, the disclosed system hashardware components (e.g. wireless transmitter/receiver with antenna,analog circuitry, power supply, processor, memory) and correspondingsoftware components. According to various embodiments of the presentteaching, the disclosed system includes Bot (referred to as a Type 1device) and Origin (referred to as a Type 2 device) for vital signdetection and monitoring. Each device comprises a transceiver, aprocessor and a memory.

The disclosed system can be applied in many cases. In one example, theType 1 device (transmitter) may be a small WiFi-enabled device restingon the table. It may also be a WiFi-enabled television (TV), set-top box(STB), a smart speaker (e.g. Amazon echo), a smart refrigerator, a smartmicrowave oven, a mesh network router, a mesh network satellite, a smartphone, a computer, a tablet, a smart plug, etc. In one example, the Type2 (receiver) may be a WiFi-enabled device resting on the table. It mayalso be a WiFi-enabled television (TV), set-top box (STB), a smartspeaker (e.g. Amazon echo), a smart refrigerator, a smart microwaveoven, a mesh network router, a mesh network satellite, a smart phone, acomputer, a tablet, a smart plug, etc. The Type 1 device and Type 2devices may be placed in/near a conference room to count people. TheType 1 device and Type 2 devices may be in a well-being monitoringsystem for older adults to monitor their daily activities and any signof symptoms (e.g. dementia, Alzheimer's disease). The Type 1 device andType 2 device may be used in baby monitors to monitor the vital signs(breathing) of a living baby. The Type 1 device and Type 2 devices maybe placed in bedrooms to monitor quality of sleep and any sleep apnea.The Type 1 device and Type 2 devices may be placed in cars to monitorwell-being of passengers and driver, detect any sleeping of driver anddetect any babies left in a car. The Type 1 device and Type 2 devicesmay be used in logistics to prevent human trafficking by monitoring anyhuman hidden in trucks and containers. The Type 1 device and Type 2devices may be deployed by emergency service at disaster area to searchfor trapped victims in debris. The Type 1 device and Type 2 devices maybe deployed in an area to detect breathing of any intruders. There arenumerous applications of wireless breathing monitoring withoutwearables.

Hardware modules may be constructed to contain the Type 1 transceiverand/or the Type 2 transceiver. The hardware modules may be sold to/usedby variable brands to design, build and sell final commercial products.Products using the disclosed system and/or method may be home/officesecurity products, sleep monitoring products, WiFi products, meshproducts, TV, STB, entertainment system, HiFi, speaker, home appliance,lamps, stoves, oven, microwave oven, table, chair, bed, shelves, tools,utensils, torches, vacuum cleaner, smoke detector, sofa, piano, fan,door, window, door/window handle, locks, smoke detectors, caraccessories, computing devices, office devices, air conditioner, heater,pipes, connectors, surveillance camera, access point, computing devices,mobile devices, LTE devices, 3G/4G/5G/6G devices, UNITS devices, 3GPPdevices, GSM devices, EDGE devices, TDMA devices, FDMA devices, CDMAdevices, WCDMA devices, TD-SCDMA devices, gaming devices, eyeglasses,glass panels, VR goggles, necklace, watch, waist band, belt, wallet,pen, hat, wearables, implantable device, tags, parking tickets, smartphones, etc.

The summary may comprise: analytics, output response, selected timewindow, subsampling, transform, and/or projection. The presenting maycomprise presenting at least one of: monthly/weekly/daily view,simplified/detailed view, cross-sectional view, small/large form-factorview, color-coded view, comparative view, summary view, animation, webview, voice announcement, and another presentation related to theperiodic/repetition characteristics of the repeating motion.

A Type 1/Type 2 device may be an antenna, a device with antenna, adevice with a housing (e.g. for radio, antenna, data/signal processingunit, wireless IC, circuits), device that has interface toattach/connect to/link antenna, device that is interfaced to/attachedto/connected to/linked to anotherdevice/system/computer/phone/network/data aggregator, device with a userinterface (UI)/graphical UI/display, device with wireless transceiver,device with wireless transmitter, device with wireless receiver,internet-of-thing (IoT) device, device with wireless network, devicewith both wired networking and wireless networking capability, devicewith wireless integrated circuit (IC), Wi-Fi device, device with Wi-Fichip (e.g. 802.11a/b/g/n/ac/ax standard compliant), Wi-Fi access point(AP), Wi-Fi client, Wi-Fi router, Wi-Fi repeater, Wi-Fi hub, Wi-Fi meshnetwork router/hub/AP, wireless mesh network router, adhoc networkdevice, wireless mesh network device, mobile device (e.g.2G/2.5G/3G/3.5G/4G/LTE/5G/6G/7G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA,CDMA, WCDMA, TD-SCDMA), cellular device, base station, mobile networkbase station, mobile network hub, mobile network compatible device, LTEdevice, device with LTE module, mobile module (e.g. circuit board withmobile-enabling chip (IC) such as Wi-Fi chip, LTE chip, BLE chip), Wi-Fichip (IC), LTE chip, BLE chip, device with mobile module, smart phone,companion device (e.g. dongle, attachment, plugin) for smart phones,dedicated device, plug-in device, AC-powered device, battery-powereddevice, device with processor/memory/set of instructions, smartdevice/gadget/items: clock, stationary, pen, user-interface, paper, mat,camera, television (TV), set-top-box, microphone, speaker, refrigerator,oven, machine, phone, wallet, furniture, door, window, ceiling, floor,wall, table, chair, bed, night-stand, air-conditioner, heater, pipe,duct, cable, carpet, decoration, gadget, USB device, plug, dongle,lamp/light, tile, ornament, bottle, vehicle, car, AGV, drone, robot,laptop, tablet, computer, harddisk, network card, instrument, racket,ball, shoe, wearable, clothing, glasses, hat, necklace, food, pill,small device that moves in the body of creature (e.g. in blood vessels,in lymph fluid, digestive system), and/or another device. The Type 1device and/or Type 2 device may be communicatively coupled with: theinternet, another device with access to internet (e.g. smart phone),cloud server (e.g. hub device), edge server, local server, and/orstorage. The Type 1 device and/or the Type 2 device may operate withlocal control, can be controlled by another device via a wired/wirelessconnection, can operate automatically, or can be controlled by a centralsystem that is remote (e.g. away from home).

In one embodiment, a Type B device may be a transceiver that may performas both Origin (a Type 2 device, a Rx device) and Bot (a Type 1 device,a Tx device), i.e., a Type B device may be both Type 1 (Tx) and Type 2(Rx) devices (e.g. simultaneously or alternately), for example, meshdevices, a mesh router, etc. In one embodiment, a Type A device may be atransceiver that may only function as Bot (a Tx device), i.e., Type 1device only or Tx only, e.g., simple IoT devices. It may have thecapability of Origin (Type 2 device, Rx device), but somehow it isfunctioning only as Bot in the embodiment. All the Type A and Type Bdevices form a tree structure. The root may be a Type B device withnetwork (e.g. internet) access. For example, it may be connected tobroadband service through a wired connection (e.g. Ethernet, cablemodem, ADSL/HDSL modem) connection or a wireless connection (e.g. LTE,3G/4G/5G, WiFi, Bluetooth, microwave link, satellite link, etc.). In oneembodiment, all the Type A devices are leaf node. Each Type B device maybe the root node, non-leaf node, or leaf node.

Type 1 device (transmitter, or Tx) and Type 2 device (receiver, or Rx)may be on same device (e.g. RF chip/IC) or simply the same device. Thedevices may operate at high frequency band, such as 28 GHz, 60 GHz, 77GHz, etc. The RF chip may have dedicated Tx antennas (e.g. 32 antennas)and dedicated Rx antennas (e.g. another 32 antennas).

One Tx antenna may transmit a wireless signal (e.g. a series of probesignal, perhaps at 100 Hz). Alternatively, all Tx antennas may be usedto transmit the wireless signal with beamforming (in Tx), such that thewireless signal is focused in certain direction (e.g. for energyefficiency or boosting the signal to noise ratio in that direction, orlow power operation when “scanning” that direction, or low poweroperation if object is known to be in that direction).

The wireless signal hits an object (e.g. a living human lying on a bed 4feet away from the Tx/Rx antennas, with breathing and heart beat) in avenue (e.g. a room). The object motion (e.g. lung movement according tobreathing rate, or blood-vessel movement according to heart beat) mayimpact/modulate the wireless signal. All Rx antennas may be used toreceive the wireless signal.

Beamforming (in Rx and/or Tx) may be applied (digitally) to “scan”different directions. Many directions can be scanned or monitoredsimultaneously. With beamforming, “sectors” (e.g. directions,orientations, bearings, zones, regions, segments) may be defined relatedto the Type 2 device (e.g. relative to center location of antennaarray). For each probe signal (e.g. a pulse, an ACK, a control packet,etc.), a channel information or CI (e.g. channel impulse response/CIR,CSI, CFR) is obtained/computed for each sector (e.g. from the RF chip).In breathing detection, one may collect CIR in a sliding window (e.g. 30sec, and with 100 Hz sounding/probing rate, one may have 3000 CIR over30 sec).

The CIR may have many taps (e.g. N1 components/taps). Each tap may beassociated with a time lag, or a time-of-flight (tof, e.g. time to hitthe human 4 feet away and back). When a person is breathing in a certaindirection at a certain distance (e.g. 4 ft), one may search for the CIRin the “certain direction”. Then one may search for the tapcorresponding to the “certain distance”. Then one may compute thebreathing rate and heart rate from that tap of that CIR.

One may consider each tap in the sliding window (e.g. 30 second windowof “component time series”) as a time function (e.g. a “tap function”,the “component time series”). One may examine each tap function insearch of a strong periodic behavior (e.g. corresponds to breathing,perhaps in the range of 10 bpm to 40 bpm).

The Type 1 device and/or the Type 2 device may have externalconnections/links and/or internal connections/links. The externalconnections (e.g. connection 1110) may be associated with2G/2.5G/3G/3.5G/4G/LTE/5G/6G/7G/NBIoT, UWB, WiMax, Zigbee, 802.16 etc.The internal connections (e.g., 1114A and 1114B, 1116, 1118, 1120) maybe associated with WiFi, an IEEE 802.11 standard,802.11a/b/g/n/ac/ad/af/ag/ah/ai/aj/aq/ax/ay, Bluetooth, Bluetooth1.0/1.1/1.2/2.0/2.1/3.0/4.0/4.1/4.2/5, BLE, mesh network, an IEEE802.16/1/1a/1b/2/2a/a/b/c/d/e/f/g/h/i/j/k/l/m/n/o/p/standard.

The Type 1 device and/or Type 2 device may be powered by battery (e.g.AA battery, AAA battery, coin cell battery, button cell battery,miniature battery, bank of batteries, power bank, car battery, hybridbattery, vehicle battery, container battery, non-rechargeable battery,rechargeable battery, NiCd battery, NiMH battery, Lithium ion battery,Zinc carbon battery, Zinc chloride battery, lead acid battery, alkalinebattery, battery with wireless charger, smart battery, solar battery,boat battery, plane battery, other battery, temporary energy storagedevice, capacitor, fly wheel).

Any device may be powered by DC or direct current (e.g. from battery asdescribed above, power generator, power convertor, solar panel,rectifier, DC-DC converter, with various voltages such as 1.2V, 1.5V,3V, 5V, 6V, 9V, 12V, 24V, 40V, 42V, 48V, 110V, 220V, 380V, etc.) and maythus have a DC connector or a connector with at least one pin for DCpower.

Any device may be powered by AC or alternating current (e.g. wall socketin a home, transformer, invertor, shorepower, with various voltages suchas 100V, 110V, 120V, 100-127V, 200V, 220V, 230V, 240V, 220-240V,100-240V, 250V, 380V, 50 Hz, 60 Hz, etc.) and thus may have an ACconnector or a connector with at least one pin for AC power. The Type 1device and/or the Type 2 device may be positioned (e.g. installed,placed, moved to) in the venue or outside the venue.

For example, in a vehicle (e.g. a car, truck, lorry, bus, specialvehicle, tractor, digger, excavator, teleporter, bulldozer, crane,forklift, electric trolley, AGV, emergency vehicle, freight, wagon,trailer, container, boat, ferry, ship, submersible, airplane, air-ship,lift, mono-rail, train, tram, rail-vehicle, railcar, etc.), the Type 1device and/or Type 2 device may be an embedded device embedded in thevehicle, or an add-on device (e.g. aftermarket device) plugged into aport in the vehicle (e.g. OBD port/socket, USB port/socket, accessoryport/socket, 12V auxiliary power outlet, and/or 12V cigarette lighterport/socket).

For example, one device (e.g. Type 2 device) may be plugged into 12Vcigarette lighter/accessory port or OBD port or the USB port (e.g. of acar/truck/vehicle) while the other device (e.g. Type 1 device) may beplugged into 12V cigarette lighter/accessory port or the OBD port or theUSB port. The OBD port and/or USB port can provide power, signalingand/or network (of the car/truck/vehicle). The two devices may jointlymonitor the passengers including children/babies in the car. They may beused to count the passengers, recognize the driver, detect presence ofpassenger in a particular seat/position in the vehicle.

In another example, one device may be plugged into 12V cigarettelighter/accessory port or OBD port or the USB port of acar/truck/vehicle while the other device may be plugged into 12Vcigarette lighter/accessory port or OBD port or the USB port of anothercar/truck/vehicle.

In another example, there may be many devices of the same type A (e.g.Type 1 or Type 2) in many heterogeneous vehicles/portable devices/smartgadgets (e.g. automated guided vehicle/AGV, shopping/luggage/movingcart, parking ticket, golf cart, bicycle, smart phone, tablet, camera,recording device, smart watch, roller skate, shoes, jackets, goggle,hat, eye-wear, wearable, Segway, scooter, luggage tag, cleaning machine,vacuum cleaner, pet tag/collar/wearable/implant), each device eitherplugged into 12V accessory port/OBD port/USB port of a vehicle orembedded in a vehicle. There may be one or more device of the other typeB (e.g. B is Type 1 if A is Type 2, or B is Type 2 if A is Type 1)installed at locations such as gas stations, street lamp post, streetcorners, tunnels, multi-storey parking facility, scattered locations tocover a big area such as factory/stadium/train station/shoppingmall/construction site. The Type A device may be located, tracked ormonitored based on the TSCI.

The area/venue may have no local connectivity, e.g., broadband services,WiFi, etc. The Type 1 and/or Type 2 device may be portable. The Type 1and/or Type 2 device may support plug and play.

Pairwise wireless links may be established between many pairs ofdevices, forming the tree structure. In each pair (and the associatedlink), a device (second device) may be a non-leaf (Type B). The otherdevice (first device) may be a leaf (Type A or Type B) or non-leaf (TypeB). In the link, the first device functions as a bot (Type 1 device or aTx device) to send a wireless signal (e.g. probe signal) through thewireless multipath channel to the second device. The second device mayfunction as an Origin (Type 2 device or Rx device) to receive thewireless signal, obtain the TSCI and compute a “linkwise analytics”based on the TSCI.

The present teaching discloses a WiFi-based passive fall detectionsystem (hereinafter “DeFall”) that can work independent of itsenvironment and free of prior training in new environments. Unlikeprevious works, the disclosed DeFall system can probe the physiologicalfeatures inherently associated with human falls, i.e., the distinctivepatterns of speed and acceleration during a fall. In some embodiments,the DeFall system comprises an offline template-generating stage and anonline decision-making stage, both taking the speed estimates as input.In the offline stage, augmented Dynamic Time Warping (DTW) algorithmsare performed to generate a representative template of the speed andacceleration patterns for a typical human fall. In the online phase, thesystem may compare the patterns of the real-time speed/accelerationestimates against the template to detect falls. To evaluate theperformance of DeFall, one may build a prototype using commercial WiFidevices and conducted experiments under different settings. In someembodiments, the evaluation results demonstrate that DeFall achieves adetection rate of 96% with a false alarm lower than 1.50% under bothline-of-sight (LOS) and non-LOS (NLOS) scenarios with one linkmeasurement.

FIG. 1 illustrates an exemplary wireless indoor rich-scatteringenvironment 100, according to some embodiments of the presentdisclosure. As shown in FIG. 1, in the wireless indoor rich-scatteringenvironment 100, a transmitter 110 (which may be a Bot) transmitswireless signals to a receiver 120 (which may be an Origin) through amultipath channel that is impacted by various scatterers, includingstatic scatterers 131, 132, 133, and/or dynamic scatterers 140. In someembodiments, one may estimate the speed of a motion based on astatistical model of EM wave theory assuming the practicalrich-scattering environment 100 in FIG. 1, which can make use of the CSImagnitude information.

FIG. 2 illustrates a flow chart of an exemplary method 200 forwirelessly detecting a target motion of an object, according to someembodiments of the present teaching. At operation 202, a first wirelesssignal is transmitted from a first wireless device, e.g. a transmitter,through a wireless multipath channel of a venue to a second wirelessdevice, e.g. a receiver. At operation 204, a second wireless signal isreceived by the second wireless device through the wireless multipathchannel. The second wireless signal differs from the first wirelesssignal due to the wireless multipath channel that is impacted by atarget motion of an object in the venue. At operation 206, a time seriesof channel information (TSCI) of the wireless multipath channel isobtained based on the second wireless signal, e.g. using a processor, amemory communicatively coupled with the processor and a set ofinstructions stored in the memory. At operation 208, a time series ofspatial-temporal information (TSSTI) of the object is computed based onthe TSCI. At operation 210, the target motion of the object is detectedbased on at least one of: the TSSTI or the TSCI. The order of theoperations in FIG. 2 may be changed according to various embodiments ofthe present teaching.

FIG. 3 illustrates an exemplary block diagram of a first wirelessdevice, e.g. a Bot 300, of a wireless motion detection system, accordingto one embodiment of the present teaching. The Bot 300 is an example ofa device that can be configured to implement the various methodsdescribed herein. As shown in FIG. 3, the Bot 300 includes a housing 340containing a processor 302, a memory 304, a transceiver 310 comprising atransmitter 312 and receiver 314, a synchronization controller 306, apower module 308, an optional carrier configurator 320 and a wirelesssignal generator 322.

In this embodiment, the processor 302 controls the general operation ofthe Bot 300 and can include one or more processing circuits or modulessuch as a central processing unit (CPU) and/or any combination ofgeneral-purpose microprocessors, microcontrollers, digital signalprocessors (DSPs), field programmable gate array (FPGAs), programmablelogic devices (PLDs), controllers, state machines, gated logic, discretehardware components, dedicated hardware finite state machines, or anyother suitable circuits, devices and/or structures that can performcalculations or other manipulations of data.

The memory 304, which can include both read-only memory (ROM) and randomaccess memory (RAM), can provide instructions and data to the processor302. A portion of the memory 304 can also include non-volatile randomaccess memory (NVRAM). The processor 302 typically performs logical andarithmetic operations based on program instructions stored within thememory 304. The instructions (a.k.a., software) stored in the memory 304can be executed by the processor 302 to perform the methods describedherein. The processor 302 and the memory 304 together form a processingsystem that stores and executes software. As used herein, “software”means any type of instructions, whether referred to as software,firmware, middleware, microcode, etc. which can configure a machine ordevice to perform one or more desired functions or processes.Instructions can include code (e.g., in source code format, binary codeformat, executable code format, or any other suitable format of code).The instructions, when executed by the one or more processors, cause theprocessing system to perform the various functions described herein.

The transceiver 310, which includes the transmitter 312 and receiver314, allows the Bot 300 to transmit and receive data to and from aremote device (e.g., an Origin or another Bot). An antenna 350 istypically attached to the housing 340 and electrically coupled to thetransceiver 310. In various embodiments, the Bot 300 includes (notshown) multiple transmitters, multiple receivers, and multipletransceivers. In one embodiment, the antenna 350 is replaced with amulti-antenna array 350 that can form a plurality of beams each of whichpoints in a distinct direction. The transmitter 312 can be configured towirelessly transmit signals having different types or functions, suchsignals being generated by the processor 302. Similarly, the receiver314 is configured to receive wireless signals having different types orfunctions, and the processor 302 is configured to process signals of aplurality of different types.

The Bot 300 in this example may serve as Bot 110 in FIG. 1 for detectingobject motion in a venue. For example, the wireless signal generator 322may generate and transmit, via the transmitter 312, a wireless signalthrough a wireless multipath channel impacted by a motion of an objectin the venue. The wireless signal carries information of the channel.Because the channel was impacted by the motion, the channel informationincludes motion information that can represent the motion of the object.As such, the motion can be indicated and detected based on the wirelesssignal. The generation of the wireless signal at the wireless signalgenerator 322 may be based on a request for motion detection fromanother device, e.g. an Origin, or based on a system pre-configuration.That is, the Bot 300 may or may not know that the wireless signaltransmitted will be used to detect motion.

The synchronization controller 306 in this example may be configured tocontrol the operations of the Bot 300 to be synchronized orun-synchronized with another device, e.g. an Origin or another Bot. Inone embodiment, the synchronization controller 306 may control the Bot300 to be synchronized with an Origin that receives the wireless signaltransmitted by the Bot 300. In another embodiment, the synchronizationcontroller 306 may control the Bot 300 to transmit the wireless signalasynchronously with other Bots. In another embodiment, each of the Bot300 and other Bots may transmit the wireless signals individually andasynchronously.

The carrier configurator 320 is an optional component in Bot 300 toconfigure transmission resources, e.g. time and carrier, fortransmitting the wireless signal generated by the wireless signalgenerator 322. In one embodiment, each CI of the time series of CI hasone or more components each corresponding to a carrier or sub-carrier ofthe transmission of the wireless signal. The detection of the motion maybe based on motion detections on any one or any combination of thecomponents.

The power module 308 can include a power source such as one or morebatteries, and a power regulator, to provide regulated power to each ofthe above-described modules in FIG. 3. In some embodiments, if the Bot300 is coupled to a dedicated external power source (e.g., a wallelectrical outlet), the power module 308 can include a transformer and apower regulator.

The various modules discussed above are coupled together by a bus system330. The bus system 330 can include a data bus and, for example, a powerbus, a control signal bus, and/or a status signal bus in addition to thedata bus. It is understood that the modules of the Bot 300 can beoperatively coupled to one another using any suitable techniques andmediums.

Although a number of separate modules or components are illustrated inFIG. 3, persons of ordinary skill in the art will understand that one ormore of the modules can be combined or commonly implemented. Forexample, the processor 302 can implement not only the functionalitydescribed above with respect to the processor 302, but also implementthe functionality described above with respect to the wireless signalgenerator 322. Conversely, each of the modules illustrated in FIG. 3 canbe implemented using a plurality of separate components or elements.

FIG. 4 illustrates an exemplary block diagram of a second wirelessdevice, e.g. an Origin 400, of a wireless motion detection system,according to one embodiment of the present teaching. The Origin 400 isan example of a device that can be configured to implement the variousmethods described herein. The Origin 400 in this example may serve asOrigin 120 in FIG. 1 for detecting object motion in a venue. As shown inFIG. 4, the Origin 400 includes a housing 440 containing a processor402, a memory 404, a transceiver 410 comprising a transmitter 412 and areceiver 414, a power module 408, a synchronization controller 406, achannel information extractor 420, and an optional motion detector 422.

In this embodiment, the processor 402, the memory 404, the transceiver410 and the power module 408 work similarly to the processor 302, thememory 304, the transceiver 310 and the power module 308 in the Bot 300.An antenna 450 or a multi-antenna array 450 is typically attached to thehousing 440 and electrically coupled to the transceiver 410.

The Origin 400 may be a second wireless device that has a different typefrom that of the first wireless device (e.g. the Bot 300). Inparticular, the channel information extractor 420 in the Origin 400 isconfigured for receiving the wireless signal through the wirelessmultipath channel impacted by the motion of the object in the venue, andobtaining a time series of channel information (CI) of the wirelessmultipath channel based on the wireless signal. The channel informationextractor 420 may send the extracted CI to the optional motion detector422 or to a motion detector outside the Origin 400 for detecting objectmotion in the venue.

The motion detector 422 is an optional component in the Origin 400. Inone embodiment, it is within the Origin 400 as shown in FIG. 4. Inanother embodiment, it is outside the Origin 400 and in another device,which may be a Bot, another Origin, a cloud server, a fog server, alocal server, and an edge server. The optional motion detector 422 maybe configured for detecting the motion of the object in the venue basedon motion information related to the motion of the object. The motioninformation associated with the first and second wireless devices iscomputed based on the time series of CI by the motion detector 422 oranother motion detector outside the Origin 400.

The synchronization controller 406 in this example may be configured tocontrol the operations of the Origin 400 to be synchronized orun-synchronized with another device, e.g. a Bot, another Origin, or anindependent motion detector. In one embodiment, the synchronizationcontroller 406 may control the Origin 400 to be synchronized with a Botthat transmits a wireless signal. In another embodiment, thesynchronization controller 406 may control the Origin 400 to receive thewireless signal asynchronously with other Origins. In anotherembodiment, each of the Origin 400 and other Origins may receive thewireless signals individually and asynchronously. In one embodiment, theoptional motion detector 422 or a motion detector outside the Origin 400is configured for asynchronously computing respective heterogeneousmotion information related to the motion of the object based on therespective time series of CI.

The various modules discussed above are coupled together by a bus system430. The bus system 430 can include a data bus and, for example, a powerbus, a control signal bus, and/or a status signal bus in addition to thedata bus. It is understood that the modules of the Origin 400 can beoperatively coupled to one another using any suitable techniques andmediums.

Although a number of separate modules or components are illustrated inFIG. 4, persons of ordinary skill in the art will understand that one ormore of the modules can be combined or commonly implemented. Forexample, the processor 402 can implement not only the functionalitydescribed above with respect to the processor 402, but also implementthe functionality described above with respect to the channelinformation extractor 420. Conversely, each of the modules illustratedin FIG. 4 can be implemented using a plurality of separate components orelements.

The target motion detected by the Bot and Origin shown in FIG. 3 andFIG. 4, respectively, may be a transient motion or a periodic motion. Insome embodiments, the Bot and Origin are wireless devices in theWiFi-based robust and environment-independent fall detection systemDeFall, to detect fall events. The DeFall system exploits of thephysiological patterns of body speed and accelerations during the fallevents, rather than less explainable data-driven features usedpreviously. Because a human fall event experiences different speedand/or acceleration from other daily activities, one may utilize theunique patterns of speed and acceleration to recognize fall events. Withthe feasibility to extract speed information passively from WiFi signalseven in NLOS environments, the DeFall system can perform the WiFi-basedspeed estimation. Since a fall involves a unique pattern of speedtransition and lasts for some time duration, the DeFall can use the timeseries of speed/acceleration captured continuously instead of theinstantaneous values for identifying fall events. This can tremendouslyreduce the unwanted false alarm in a real environment. The temporalvariability in time series brings up a challenge. To adapt to thenon-linear compression or stretching over time, one may apply theaugmented Dynamic Time Warping (DTW) based algorithms for the timeseries processing.

In some embodiments, the DeFall system comprises two key components: theoffline template-generating stage and the online decision-making stage.In the offline stage, a representative template for speed andacceleration series is generated. After that, the similarity betweenreal-time speed/acceleration series and the template is evaluated in theonline stage to detect a fall. Since the speed and acceleration areinherent properties of the human motion that are independent of thestatic background environment, the DeFall system needs only one-timelight training and is robust against different environments in anunsupervised manner. In addition, due to the rich-scattering model usedin speed estimation, the system can work very well under bothline-of-sight (LOS) and non-line-of-sight (NLOS) scenarios.

To evaluate the performance of DeFall, extensive experiments have beenconducted in a typical indoor environment in various settings. One mayfirst use a human-like dummy with a similar size and weight to realhuman to carry out more than 800 fall experiments under LOS and NLOS tocalculate the detection rate (DR). One may also test the false alarmrate (FAR) while the real human performs daily indoor activitiesincluding walking and sitting. Furthermore, real human fall samples areapplied to verify the feasibility of the disclosed system. Theexperimental results show that DeFall can achieve a DR of 96% with anFAR of 1.47%, outperforming existing solutions in terms of both accuracyand coverage.

While the DeFall system is the first one to leverage the time series ofspeed and acceleration to detect the falls based on WiFi devices, itworks well in both LOS and NLOS scenarios, getting rid of the limitationof coverage while also protecting privacy. Through long-term testing,the system can work independently without any re-training in a changingenvironment.

Speed and acceleration are two characteristics that are usually used todescribe motion. Intuitively, fall can be viewed as a type of abnormalindoor event with abnormal speed and acceleration. Therefore, they areboth considered as the unique characteristics that help distinguishingfalls from other daily activities. The uniqueness resides not only inthe absolute values of speed and acceleration during a fall, but also inhow they change along the time. More specifically, when a human falls tothe ground, his/her body will experience a rapid acceleration first.Once the body hits the floor, the body speed reduces to nearly zerosharply. In fact, most of the unexpected falls exhibit a similar patternand this implies the feasibility of developing anenvironment-independent system by monitoring the speed and accelerationvariation, which is utilized by the DeFall system.

Speed estimation from CSI: In wireless communication, channel stateinformation (CSI), or alternatively channel frequency response (CFR),describes the propagation of the signals from the transmitter (Tx) tothe receiver (Rx). The estimate of the CSI over a subcarrier withfrequency f at time t can be represented as:

$\begin{matrix}{{{H\left( {t,f} \right)} = \frac{Y\left( {t,f} \right)}{X\left( {t,f} \right)}},} & (1)\end{matrix}$

where X(t, f) and Y(t, f) are transmitted and received signals. Thetransmitted WiFi signals experience multiple reflections in theirpropagation in indoor environments, and therefore CSI contains a lot ofuseful information on the channel conditions, which implies that onecould capture the changes of the surrounding environment through CSI.

Since the unique pattern of the series of speed and acceleration isutilized, it is critical to have an accurate and reliable estimate ofthe speed based on WiFi CSI, which is not trivial due to the multi-patheffects of the indoor propagation. Some device-free CSI-based speedestimators make use of the Doppler Frequency Shift (DFS) to calculatethe moving speed of human body with the constraint of LOS coverage sincethe moving body should be able to be “seen” by both Tx and Rx. TheDFS-based methods are based on the assumption of a limited number ofpropagation paths, which usually does not hold in a practical indoorenvironment with rich multi-path propagation. In addition, DFS-basedspeed estimators take CSI phase into account.

However, the phase of CSI on commercial WiFi devices cannot be measuredaccurately due to the phase synchronization errors between the WiFi Txand Rx.

The disclosed system may estimate the speed based on a statistical modelof EM wave theory assuming a practical rich-scattering environment as inFIG. 1, which only makes use of the CSI magnitude information.Specifically, the CSI magnitude can be measured through CSI powerresponse G(t, f) defined as:

G(t,f)

|H(t,f)|²=ξ(t,f)+ε(t,f),  (2)

where ξ(t, f)=∥{right arrow over (E)}_(Rx)(t,f)∥², and {right arrow over(E)}_(Rx)(t, f) denotes the propagated signals. ε(t, f) notes theadditive noise, and ξ(t, f) and ε(t, f) are assumed to be independent ofeach other. It has been shown that the speed of a moving object can bereliably estimated by evaluating the autocorrelation function (ACF) of G(t, f). The theoretical ACF of G(t, f), ρ_(G)(τ, f), can be derived as:

$\begin{matrix}{{{\rho_{G}\left( {\tau,f} \right)} = {{\frac{\sigma_{\xi}^{2}(f)}{{\sigma_{\xi}^{2}(f)} + {\sigma_{ɛ}^{2}{\sigma (f)}}}{\rho_{\xi}\left( {\tau,f} \right)}} + {\frac{\sigma_{ɛ}^{2}(f)}{{\sigma_{\xi}^{2}(f)} + {\sigma_{ɛ}^{2}(f)}}{\delta (\tau)}}}},} & (3)\end{matrix}$

where τ is the time lag of the ACF. σ_(ξ) ²(f) and σ_(ε) ²(f) are thevariances of ξ(t, f) and ε(t, f), respectively. ρ_(ξ)(τ, f) and Diracdelta function δ(⋅) are the ACFs of ξ(t, f) and ε(t, f). When τ≠0, onecould have δ(τ)=0 and ρ_(G)(τ, f) can be further derived based on thestatistical theory of EM waves as:

ρ_(G)(τ,f)=Σ_(u∈{x,y,z})(C ₁(f)ρ_(E) _(u) (τ,f)+C ₂(f)ρ_(E) _(u)²(τ,f)),  (4)

where C₁(f) and C₂ (f) are scaling factors determined by the powerreflected by all scatterers. ρ_(E) _(u) (τ, f) is the ACF of {rightarrow over (E)}_(Rx) (t, f) in u-axis direction where u∈{x, y, z}.

For the i-th dynamic scatterer that moves at speed v_(i) along z-axis,the scattered signal is denoted as Ē_(iu)(t, f). Then the components ofits ACF ρ_(E) _(iu) (τ, f) in {x, y, z}-axes can be expressed as thefollowing closed-form equations, respectively:

$\begin{matrix}\begin{matrix}{{\rho_{E_{ix}}\left( {\tau,f} \right)} = {\rho_{E_{iy}}\left( {\tau,f} \right)}} \\{= {{\frac{3}{2}\frac{\sin \left( {{kv}_{i}\tau} \right)}{{kv}_{i}\tau}\left( {1 - \frac{1}{\left( {{kv}_{i}\tau} \right)^{2}}} \right)} + {\frac{3}{2}\frac{\cos \left( {{kv}_{i}\tau} \right)}{\left( {{kv}_{i}\tau} \right)^{2}}}}}\end{matrix} & (5) \\{{\rho_{E_{iz}}\left( {\tau,f} \right)} = {\frac{3}{\left( {kv_{i}\tau} \right)^{2}}\left( {\frac{\sin \left( {{kv}_{i}\tau} \right)}{{kv}_{i}\tau} - {\cos \left( {kv_{i}\tau} \right)}} \right)}} & (6)\end{matrix}$

where k denotes the wave number. Intuitively, the equations above haveestablished a relationship between the ACF ρ_(G)(τ, f) and the presenceof motion and also the moving speed.

The relationship between ρ_(G) (τ, f) and the presence of motion isdescribed here. From (3), if motion is present in the propagationenvironment of WiFi signals, as τ→0, one could have δ(τ)=0 and ρ_(ξ)(τ,f)→1 due to the property of white noise and the continuity of motion.Consequently,

$\begin{matrix}\left. {p_{G}\left( {\tau,f} \right)}\rightarrow\left. {\frac{\sigma_{\xi}^{2}(f)}{{\sigma_{\xi}^{2}(f)} + {\sigma_{ɛ}^{2}(f)}} > {0\mspace{14mu} {as}\mspace{14mu} T}}\rightarrow 0. \right. \right. & (6)\end{matrix}$

If there is no motion, the environment is static and the variance σ_(ξ)²(τ,f)=0 and thus p_(G) (τ,f)=0 as τ→0. Therefore the value oflim_(τ→0)ρ_(G)(τ,f) can indicate the presence of motion in thesurrounding environment.

The relationship between p_(G) (τ, f) and the moving speed is describedhere. For the simple case of all dynamic scatterers moving in the samespeed and direction, without loss of generality, one can assume themoving direction is in the z-axis and get the p_(G)(τ, f) as (4) withits components expressed in (5) and (6). Each component and itsdifferential can be visualized in FIG. 5A and FIG. 5B, respectively.

FIGS. 5A-5D illustrate an exemplary spatial auto-correlation function(ACF) and its differentials for electromagnetic (EM) wave components,according to one embodiment of the present teaching. FIG. 5A shows thetheoretical spatial ACFs; FIG. 5B shows the theoretical differentialspatial ACFs; FIG. 5C shows the instance of differential spatial ACFs ofCSI power; and FIG. 5D shows the differential ACF of CSI power and thevalley location.

Observing that the first local valley of ρ_(E) _(u) ²(τ, f), ∀u∈{x, y},happens to be the first local peak of Δp_(G)(τ) as well, one can extractthe speed information of the moving scatterers by locating the firstlocal valley of p_(G)(τ, f). In the case where a single subject, e.g. ahuman, moves within the coverage of the pair of Rx and Tx, the dynamicsignals are dominated by the parts that are reflected by the human body.Therefore, it is reasonable to assume that in this case, all dynamicscatterers are moving at the same speed as well as in the samedirection, and one can estimate the speed of the human during a fallusing the disclosed method and further detect a fall.

The DeFall system mainly includes two stages as illustrated in FIG. 6:an offline template-generating stage 610 and an online decision-makingstage 620. In the offline stage 610, the speed of a fall is estimated atoperation 611 from the WiFi CSI by applying a statistical model on theradio propagation in an indoor rich-scattering environment. Then DynamicTime Warping (DTW) based algorithms may be performed to generate arepresentative template for a typical human fall. The representativetemplate may be a two-dimensional (2D) template 616 including both speedand acceleration patterns 615.

For example, a segmental locally normalized dynamic time warping(SLN-DTW) algorithm and a DTW barycenter averaging (DBA) algorithm maybe performed at operations 612 and 613 respectively to generate a speedtemplate. An acceleration template may be generated at operation 614based on the speed template generated at operation 613. The 2D template616 includes information from both the speed template and theacceleration template.

Then a fall event can be detected in the online stage 620 by evaluatingthe similarity between the patterns of real-time speed/accelerationestimates 623, 624 and the representative template 616. In addition, anonline motion detection module 622 is added before the fall detection asa pre-judgment procedure. In this embodiment, the fall detection isperformed only after a presence of a motion is detected by the module622.

In some embodiments, during the offline template-generating stage 610, MCSI sequences of fall events are picked randomly and a “templatedatabase”

={S₁, S₂, . . . , S_(M)} is built based on the corresponding estimatedspeed series. To construct a single representative template, one mayperform an “average” on the database. Since the collected data are alltime sequences, the result by direct point-to-point matching andaveraging will be easily affected by sequence shift and misalignment.Therefore, the operation of distance measurement, as well as seriesalignment, will be performed in the DTW space.

There may be redundant speed segments of other activities before orafter the fall event, and the classic DTW algorithm is sensitive to theendpoints of the sequences. Therefore, the endpoints of the series maybe carefully defined and the template database cleaning may beperformed.

Template database cleaning: To remove the redundancy while adapting tothe possible variability in event instances, one may resort to theband-relaxed segmental locally normalized dynamic time warping(SLN-DTW). FIG. 7A and FIG. 7B illustrate an instance of the sanitizedspeed series by applying SLN-DTW.

Averaging in the DTW measure space: The M cleaned speed series in therefined database

are then scaled to the same length and averaged in the DTW measure spaceto construct a single representative profile. The problem to find anoptimal average can be formulated as an optimization problem that givena set of template time series

={Ŝ₁, Ŝ₂, . . . , Ŝ_(M)}, the averaged series S is the series thatminimizes the sum of squared DTW distances between S and all ofsequences in

as:

$\begin{matrix}{S = {\underset{S}{\arg \; \min}{\sum_{x = 1}^{M}{{{DTW}^{2}\left( {S,S_{x}} \right)}.}}}} & (11)\end{matrix}$

The DTW distance of two sequences DTW (A, B) is defined as the Euclideandistance between series A and series B along the optimal warping path asfollows:

DTW(A,B)=√{square root over (Σ_(p*=1) ^(|P*|) ∥A[a _(p*)]−B[b_(p*)]∥²,)}  (12)

where P* is the optimal warping path that minimizes the normalizeddistance:

$\begin{matrix}{{P^{*} = {\min\limits_{P}{\frac{1}{P}{\sum_{p = 1}^{P}{{{A\left\lbrack a_{p} \right\rbrack} - {B\left\lbrack b_{p} \right\rbrack}}}^{2}}}}},} & (13)\end{matrix}$

where a_(p) and b_(p) are indices of A and B associated with the p-thpoint on path P.

To solve the minimization problem in (11) and get the optimal averageseries, DTW Barycenter Averaging (DBA) algorithm may be implemented. DBAis an iterative algorithm that refines an average sequence S on eachiteration following an expectation-maximization scheme, whoseconvergence has been proved. The optimal speed time series S, producedby DBA, is then considered as the speed template.

Besides speed, acceleration depicts the motion during a fall fromanother different point of view. To get a more comprehensive descriptionof the fall events, one may derive an acceleration series S′ from thespeed template S and combine them by point-to-point stitching togenerate a 2D template S _(2D).

Decision-making stage: Fall events experience distinct speed andacceleration patterns which could be used for distinguishing falls fromother indoor daily activities. Since a high sampling rate is needed forspeed estimation, in the decision-making stage, a low-rate motiondetection (MD) module is included in addition to the fall detection (FD)module to save energy and computation.

Motion detection module: As lim_(τ→0)ρ_(G)(τ, f) could be utilized as acriterion for MD, one could only use

$\rho_{G}\left( {{\tau = \frac{1}{F_{s}}},f} \right)$

to approximate τ→0. For the purpose of efficient energy-saving, the MDmodule is added as a pre-detection of human motion prior to the FDmodule, and the FD module is triggered only in the presence of motion.

Fall detection module: In the FD module, one may apply a sliding window

on the incoming CSI stream. The testing speed sequence T is estimatedfrom the CSI series in window

. The acceleration sequence T′ is further derived from T, followed by acombination operation to form a 2-D pattern T_(2D).

Then fall events can be detected by comparing the testing time seriesT_(2D) with the template S _(2D). The corresponding similarity of thetwo series is evaluated in DTW space to adapt to misalignment of the twosequences in the time domain.

Since the fall events involving different people may experiencedifferent duration, the series segmented by a length-fixed slidingwindow may also include other activities before or after the targetevent, which cannot be handled by a traditional DTW. Thus, one may adoptthe segmental locally normalized DTW (SLN-DTW) again to localize thestart and end instances of an event. Regarding the template S _(2D) asthe objective series and T_(2D) as the testing series, one may set thelengths of starting and ending bands of S_(2D) to be 1, since thetemplate S _(2D) is already sanitized.

By implementing SLN-DTW, the similarity of the testing stream T_(2D) andS _(2D) is evaluated. When the DTW distance between the testing seriesand the reference template is less than a preset empirical threshold y,the testing sequence T_(2D) has a similar pattern to the reference falltemplate S _(2D) and the detector will alert that a fall occurs, where yis decided by experiments as well as the requirement of FAR and DR.

According to some embodiments, in the real-time monitoring, MD modulekeeps running with a lower sampling rate. As long as the motion isdetected, the FD module starts working with a high sampling rate todetect fall events. When the estimated speed stays lower than somethreshold for a long enough time, it can switch back to MD module tosave power consumption and computation cost.

According to some embodiments, one prototype of DeFall is implementedbased on the commodity off-the-shelf (COTS) WiFi devices at carrierfrequency 5.808 GHz with a 40 MHz bandwidth. The detailed setup for thisexperiment is presented in FIG. 8 with the locations of Tx and Rxmarked, where one may conduct the experiments under LOS scenario andNLOS scenario, respectively.

Under LOS scenario, where Tx and Rx could both “see” the object, the Txis deployed in position Tx₁. The falls of the dummy are performed closeto the direct link of Tx and Rx.

Under NLOS scenario, there does not exist any direct link between thesubject and one or more devices, which is very common for an indoorenvironment. The Tx is deployed on position Tx₂ while the position of Rxkeeps the same as that under LOS scenario. In order to guarantee theblock of the direct path, tinfoil foil is placed on the wall between Tx₂and the subject. The data collection is carried out on different dayslasting for more than three months, during which the surroundingpropagation environment keeps changing, including the changes of theplacement of furniture, the opening or closing of doors and windows,etc. To validate the feasibility of DeFall, one may first use ahuman-like dummy to collect both the template data and testing data.After that, the samples from real human falls are evaluated to furtherverify the efficacy of the system.

In the experiments, two types of fall events are considered,“stand-then-fall” and “walk-then-fall”. “Stand-then-fall” is realized byfirst letting the dummy stand straight and then making it fall freely;while “walk-then-fall” requires the experimenter to walk around thestanding dummy at a normal speed and then make it fall. An instance ofthe speed and acceleration patterns for “walk-then-fall” is presented inFIG. 9A and FIG. 9B respectively. For non-falls, daily activities withhigh speeds are taken into consideration including walking and sitting.FIG. 9C and FIG. 9D indicate an instance of the speed and accelerationpatterns for “sitting-down”. After the long-term data collection, thereare 846 fall samples and 814 non-fall samples in total.

The generated template after refinement and averaging is presented inFIGS. 10A-10C, showing that the speed rises to a peak value first andthen drops, while the acceleration is positive first and then becomesnegative. FIG. 10A shows the template speed series; FIG. 10B shows thetemplate acceleration series; and FIG. 10C shows the template in thetwo-dimensional space.

The evaluation metrics of the system performance are detection rate andfalse alarm rate. Detection rate, shorted as DR, is defined as thepercentage of correctly detected falls among all falls, while falsealarm rate, simplified as FAR, is the percentage of non-falls that aremistaken as falls among all non-falls. A threshold-based method may beapplied to detect falls and two features are disclosed: (i) the maximumspeed; (ii) the maximum change in acceleration within 0.5 s. To show theefficiency of the DeFall system, one may compare its receiver operatingcharacteristic (ROC) curve with that of the threshold-based method inFIG. 11. FIG. 11 illustrates the ROC curve for a dynamic time warping(DTW) method disclosed in the present teaching vs. a threshold method,according to one embodiment of the present teaching. As FIG. 11illustrates, at the same level of FAR, the DR of DeFall is higher thanthe threshold-based method. The Area Under the Curve (AUC) of the ROC ofDeFall is larger as well, proving a better performance. In particular,indicated by the magnified part in FIG. 11, when the FAR is less than1.5%, DeFall can still achieve a high DR over 95% while thecorresponding DR of threshold-based method drops to a level less than75%.

The results of DR and FAR for all types of events are summarized inTable 1. According to the results listed in Table 1, DeFall succeeds inperforming a high DR and low FAR under both LOS and NLOS scenarios.Comparing the results of different fall events, one can note higher DRon “stand-then-fall” events than “walk-then-fall” events, since“walk-then-fall” may introduce interference to the speed estimation atthe beginning of falls. In addition, among the non-fall events, as“sitting-down” experiences an acceleration followed by a deceleration,which is more similar to the fall pattern than “walking”, it can beobserved that FAR of “sitting-down” is slightly higher than that of“walking”.

TABLE 1 Experimental results in terms of FAR and DR Scenario Events DRFAR LOS Fall Stand-then-Fall 97.40% 97.10% — — Walk-then-Fall 95.74% —non-Fall Walking — — 0.00% 1.45% Sitting down — 2.82% NLOS FallStand-then-Fall 98.15% 97.56% — — Walk-then-Fall 94.83% — non-FallWalking — — 0.47% 1.49% Sitting down — 2.33%

In some embodiments, in order to prove that our system can come in handyin real application, two volunteers are asked to perform real fallsunder the protection of a thin medium-firm cushion on the ground. Thereis a total of 100 real fall samples collected from one male and onefemale. Using the template generated from the dummy falls and taking thethreshold selected based on the results above, the DR for real fallsachieves 96%, which further demonstrates the independence of DeFall fromenvironments and subjects, and presents its great potential forreal-world deployment.

The following numbered clauses provide implementation examples forperiodic or transient motion detection.

Clause 1. A method of a wireless target motion detection system,comprising: transmitting a wireless signal from a Type1 heterogeneouswireless device in a venue through a wireless multipath channel of thevenue, wherein an object in the venue is undergoing a target motion;receiving the wireless signal by a Type2 heterogeneous wireless devicein the venue through the wireless multipath channel, wherein thereceived wireless signal differs from the transmitted wireless signaldue to the wireless multipath channel of the venue and a modulation ofthe wireless signal by the object undergoing the target motion in thevenue; obtaining a time series of channel information (TSCI) of thewireless multipath channel based on the received wireless signal using aprocessor, a memory communicatively coupled with the processor and a setof instructions stored in the memory; and detecting the target motion ofthe object based on the TSCI.

Clause 2. The method of the wireless target motion detection system ofclause 1, comprising: computing a time series of spatial-temporalinformation (STI) of the object based on the TSCI, wherein a STIcomprises at least one of: location, position, change in position,distance, change in distance, speed, change in speed, acceleration,change in acceleration, orientation, direction, change in direction,angle, change in angle, angular speed, change in angular speed, angularacceleration, change in angular acceleration, size, change in size,shrinking, expansion, shape, change in shape, deformation, andtransformation; and detecting the target motion of the object based onthe time series of STI (TSSTI).

Clause 3. The method of the wireless target motion detection system ofclause 2, comprising: computing a time series of features (TSF) based onthe TSCI, wherein a feature comprises at least one of: a quantity,scalar, vector, matrix, data structure, temporal feature, frequencyfeature, time-frequency feature, dominant feature, representativefeature, characteristic feature, typical feature, atypical feature,magnitude, phase, magnitude of a component of a CI (CI component), phaseof the CI component, similarity measure, similarity score, similaritybetween two CI, similarity between two vectors of CI, similarity betweenCI components, distance measure, distance score, distance between twoCI, distance between two vectors of CI, distance between CI components,Euclidean distance, absolute distance, L-1 distance, L-2 distance, L-kdistance, weighted distance, graph distance, distance metric, norm, L-1norm, L-2 norm, L-k norm, correlation, correlation of two CI,correlation between two vectors of CI, correlation between CIcomponents, autocorrelation function (ACF), feature of ACF, spectrum,feature of spectrum, spectrogram, feature of spectrogram, local maximum,local minimum, zero crossing, correlation coefficient, inner product,dot product, outer product, covariance, auto-covariance, crosscovariance, discrimination score, mean, variance, standard deviation,derivative, variation, total variation, spread, dispersion, variability,deviation, total deviation, divergence, entropy, range, skewness,kurtosis, variance-to-mean ratio, maximum-to-minimum ratio, likelihood,repeatedness, periodicity, impulsiveness, sudden-ness, recurrence,period, time, timing, starting time, initiating time, ending time,duration, or time trend; and computing the TSSTI based on the TSF.

Clause 4. The method of the wireless target motion detection system ofclause 3, comprising: wherein the object is a human; wherein the targetmotion is a fall-down motion of the object; wherein a feature of the TSFcomprises at least one of: a component of a CI, a magnitude of acomponent of a CI, an autocorrelation function of the TSCI; wherein theSTI comprises at least one of: the speed or the acceleration.

Clause 5. The method of the wireless target motion detection system ofclause 2, comprising: in an offline stage, computing at least onerepresentative STI template based on a set of training TSSTI, eachtraining TSSTI computed based on a respective training TSCI obtainedbased on a respective training wireless signal from a respectivetraining Type1 device received by a respective training Type2 device ina respective training venue when a respective training object in therespective training venue undergoes a respective training target motion;and in an online stage, detecting the target motion of the object basedon the TSSTI and the at least one representative STI template.

Clause 6. The method of the wireless target motion detection system ofclause 5, comprising: cleaning the set of training TSSTI; computing theat least one representative STI template based on the set of cleanedtraining TSSTI.

Clause 7. The method of the wireless target motion detection system ofclause 6, comprising: cleaning each training TSSTI by segmenting theTSSTI into a respective initial non-target segment of STI, a respectivetarget segment of STI, and a respective trailing non-target segment ofSTI, wherein the target segment of STI corresponds to the respectivetraining target motion of the respective training object.

Clause 8. The method of the wireless target motion detection system ofclause 7, comprising: segmenting the TSSTI based on at least one of: arespective first constraint on the respective initial non-targetsegment, a respective second constraint on the respective targetsegment, or a respective third constraint on the respective trailingnon-target segment.

Clause 9. The method of the wireless target motion detection system ofclause 8, comprising at least one of: constraining the respectiveinitial non-target segment to start from the beginning of the TSSTI andto have a first duration that is at least one of: less than a firstthreshold, or being a fraction of a duration of the TSSTI; constrainingthe respective target segment to have a second duration not greater thana target duration associated with the target motion of the object; orconstraining the respective trailing non-target segment to end at theend of the TSSTI and to have a third duration being another fraction ofthe duration of the TSSTI.

Clause 10. The method of the wireless target motion detection system ofclause 7, comprising: segmenting the training TSSTI based on a mappingbetween the training TSSTI and another training TSSTI with a mappingscore between the two TSSTI associated with the mapping, wherein eachSTI of training TSSTI is mapped to at least one STI in the mapping,wherein the mapping score comprises at least one of: a similarity scoreand a mismatch score.

Clause 11. The method of the wireless target motion detection system ofclause 10, comprising: computing a plurality of candidate mappingsbetween the two TSSTI and a plurality of mapping scores associated withthe candidate mappings, wherein each candidate mapping is between acandidate target segment of the training TSSTI and another candidatetarget segment of the another training TSSTI, and a mapping scoreassociated with the candidate mapping is normalized based on a lengthassociated with the candidate target segment and the another candidatetarget segment; computing the mapping based on the plurality ofcandidate mappings and the associated mapping scores.

Clause 12. The method of the wireless target motion detection system ofclause 11, comprising: determining a particular candidate mappingassociated with an optimal mapping score among all the candidatemappings, wherein the optimal mapping score comprises at least one of: amaximum similarity score and a minimum mismatch score; and choosing theparticular candidate mapping as the mapping between the two TSSTI andthe associated optimal mapping score as the associated mapping scorebetween the two TSSTI.

Clause 13. The method of the wireless target motion detection system ofclause 11, comprising: computing iteratively the plurality of mappingscores associated with the number of candidate mappings; determining aseries of partial mappings between subsets of the training TSSTI andsubsets of the another training TSSTI, and computing iteratively aquantity associated with each partial mapping, wherein each partialmapping is a mapping between a partial segment of the training TSSTI anda partial segment of the another training TSSTI, wherein the series ofpartial mappings comprises the plurality of candidate mappings; in aninitial iteration, initializing iteration by determining an initialpartial mapping between a single STI of the training TSSTI and a singleSTI of the another training TSSTI and computing an initial quantityassociated with the initial partial mapping; in subsequent iterations,computing iteratively the quantity associated with each partial mappingbased on a value associated with another partial mapping computed in aprevious iteration, wherein the partial mapping is between a firstsegment of the training TSSTI and a second segment of the anothertraining TSSTI, and the another partial mapping is between a thirdsegment of the training TSSTI and a fourth segment of the another TSSTI,wherein at least one of: the third segment is a subset of the firstsegment, or the fourth segment is a subset of the second segment;computing the plurality of mapping scores based on the quantitiesassociated with the series of partial mappings.

Clause 14. The method of the wireless target motion detection system ofclause 11, comprising: computing iteratively the plurality of mappingscores associated with the number of candidate mappings; determining aseries of partial mappings between subsets of the training TSSTI andsubsets of the another training TSSTI, and computing iteratively aquantity associated with each partial mapping, wherein each partialmapping is a mapping between a partial segment of the training TSSTI anda partial segment of the another training TSSTI, wherein the series ofpartial mappings comprises the plurality of candidate mappings; in aninitial iteration, initializing iteration by determining a group ofinitial partial mappings comprising a mapping between a single STI ofthe training TSSTI and a single STI of the another training TSSTI andcomputing initial quantities associated with the group of initialpartial mappings; in subsequent iterations, computing iterativelyquantities associated with a group of partial mappings based on at leastone value computed in at least one previous iteration; computing theplurality of mapping scores based on the quantities associated with theseries of partial mappings.

Clause 15. The method of the wireless target motion detection system ofclause 14, comprising: wherein the quantity associated with a partialmapping is a function of a measure component associated with the partialmapping and a length component associated with the partial mapping;wherein the measure component is at least one of: a similarity componentand a mismatch component; in the initial iteration, computing an initialmeasure component and an initial length component associated with theinitial partial mapping; in subsequent iterations, computing iterativelya measure component and a length component associated with each partialmapping based on at least one of: another measure component computed ina past iteration or another length component computed in another pastiteration, and computing the quantity as the function of the measurecomponent and the length component.

Clause 16. The method of the wireless target motion detection system ofclause 10, comprising: segmenting the training TSSTI based on aplurality of pairwise mappings, wherein each pairwise mapping is amapping between the training TSSTI and one of the rest of the trainingTSSTI; computing a plurality of candidate segmentations of the trainingTSSTI each based on a respective pairwise mapping, each candidatesegmentation comprising a candidate initial non-target segment, acandidate target segment, and a candidate trailing non-target segment ofthe training TSSTI; segmenting the training TSSTI by combining theplurality of candidate segmentations.

Clause 17. The method of the wireless target motion detection system ofclause 16, comprising: determining two respective candidate boundarypoints of each candidate segmentation: a respective candidate firstboundary point between the respective candidate initial segment and therespective candidate target segment, and a respective candidate secondboundary point between the respective candidate target segment and therespective candidate trailing segment of the candidate segmentation;segmenting the training TSSTI by computing a first boundary pointbetween the initial segment and the target segment of the training TSSTIas a first function of the candidate first boundary points of theplurality of candidate segmentation; segmenting the training TSSTI bycomputing a second boundary point between the target segment and thetrailing segment of the training TSSTI as a second function of thecandidate second boundary points of the plurality of candidatesegmentation.

Clause 18. The method of the wireless target motion detection system ofclause 17: wherein at least one of: the first function or the secondfunction comprises at least one of: a mean, median, mode, weightedaverage, centroid, arithmetic mean, geometric mean, harmonic mean,trimmed mean, conditional mean, maximum, minimum, percentile, maximumlikelihood, and transformation.

Clause 19. The method of the wireless target motion detection system ofclause 7: computing a representative STI template based on at least oneof: the target segments of the cleaned training TSSTI, a time derivativeof the target segments, or a signal processing of the target segments.

Clause 20. The method of the wireless target motion detection system ofclause 19: computing the representative STI template by computing aTSSTI that optimizes a combined score with respect to the set of cleanedtraining TSSTI; wherein optimizing comprises at least one of:maximization, minimization, constrained maximization, or constrainedminimization; wherein the combined score comprises an aggregation of atleast one of: a plurality of first scores each being a first pairwisescore between the TSSTI and a cleaned training TSSTI based on a pairwisemapping between the pair, a plurality of second scores each being asecond pairwise score between the time derivative of the TSSTI and thetime derivative of a cleaned training TSSTI based on the pairwisemapping, or a plurality of third scores each being a third pairwisescore between the signal processed TSSTI and a signal processed cleanedtraining TSSTI based on the pairwise mapping; wherein the aggregationcomprising at least one of: sum, product, mean, median, mode, arithmeticmean, geometric mean, harmonic mean, trimmed mean, weighted sum,weighted product, weighted mean, weighed median, weighted mode, ordiscriminative cost; wherein the discriminative cost comprising aweighted difference between a first normalized aggregation of a firstpairwise mapping score between the TSSTI and each of a first subset ofthe cleaned training TSSTI and a second normalized aggregation of asecond pairwise mapping score between the TSSTI and each of a secondsubset of the cleaned training TSSTI; wherein any normalized aggregationcomprises at least one of: mean, median, mode, arithmetic mean,geometric mean, harmonic mean, trimmed mean, weighted mean, weightedmedian or weighted mode.

Clause 21. The method of the wireless target motion detection system ofclause 5, comprising: in the offline stage, computing the at least onerepresentative STI template based on a first dynamic time warping (DTW);in the online stage: computing a test mapping between the TSSTI and eachrepresentative STI template with respective associated test mappingscore based on a second DTW; detecting the target motion of the objectif a test mapping score satisfies a detection criterion.

Clause 22. The method of the wireless target motion detection system ofclause 2, comprising: detecting a presence of motion based on at leastone of: the TSCI, and the TSSTI before detecting the target motion ofthe object.

Clause 23. The method of the wireless target motion detection system ofclause 22, comprising: wherein the wireless signal comprises a timeseries of probing signals with piecewise-constant instantaneous proberates corresponding to piecewise-constant instantaneous inter-probeperiods; wherein the CI of the TSCI have respective piecewise-constantinstantaneous CI rates corresponding to the piecewise-constantinstantaneous probe rates; wherein the STI of the TSSTI have respectivepiecewise-constant instantaneous STI rates corresponding to thepiecewise-constant instantaneous CI rates of the TSCI; in a standbymode, configuring the instantaneous STI rates, and the instantaneous CIrates to be a default rate by configuring the instantaneous probe rateto be the default rate; in the standby mode, detecting a presence ofmotion based on at least one of: the CI at the default rate, or the STIat the default rate; in a task mode, configuring the instantaneous STIrates, and the instantaneous CI rates to be an elevated rate byconfiguring the instantaneous probe rate to be the elevated rate,wherein the elevated rate is greater than the default rate; in the taskmode, detecting the target motion of the object based on at least oneof: the CI at the elevated rate, or the STI at the elevated rate;changing between the task mode and the standby mode.

Clause 24. A Type2 heterogeneous wireless device of a wireless targetmotion detection system, comprising: a wireless receiver; a processorcommunicatively coupled with the wireless receiver; a memorycommunicatively coupled with the processor; a set of instructions storedin the memory which, when executed by the processor, cause the Type2heterogeneous wireless device in a venue to perform: receiving awireless signal through a wireless multipath channel of the venue,wherein an object undergoes a target motion in the venue, wherein thewireless signal is transmitted from a Type1 heterogeneous wirelessdevice in the venue, wherein the received wireless signal differs fromthe transmitted wireless signal due to the wireless multipath channel ofthe venue and a modulation of the wireless signal by the objectundergoing the target motion in the venue, obtaining a time series ofchannel information (TSCI) of the wireless multipath channel based onthe received wireless signal, computing a time series ofspatial-temporal information (TSSTI) of the object based on the TSCI,and detecting the target motion of the object based on at least one of:the TSSTI, or the TSCI.

Clause 25. The Type2 heterogeneous wireless device of the wirelesstarget motion detection system of clause 24, wherein the set ofinstructions further case the Type2 device to perform: in an offlinestage, computing at least one representative STI template based on a setof training TSSTI, each training TSSTI computed based on a respectivetraining TSCI obtained based on a respective training wireless signalfrom a respective training Type1 device received by a respectivetraining Type2 device in a respective training venue when a respectivetraining object in the respective training venue undergoes a respectivetraining target motion; and in an online stage, detecting the targetmotion of the object based on the TSSTI and the at least onerepresentative STI template.

Clause 26. The Type2 heterogeneous wireless device of the wirelesstarget motion detection system of clause 25, wherein the set ofinstructions further case the Type2 device to perform: cleaning eachtraining TSSTI by segmenting the TSSTI into a respective initialnon-target segment of spatial-temporal information (STI), a respectivetarget segment of STI, and a respective trailing non-target segment ofSTI, wherein the target segment of STI corresponds to the respectivetraining target motion of the respective training object; segmenting thetraining TSSTI based on a mapping between the training TSSTI and anothertraining TSSTI, and a mapping score between the two TSSTI associatedwith the mapping, wherein each STI of the training TSSTI is mapped to atleast one STI in the mapping, wherein the mapping score comprises atleast one of: a similarity score and a mismatch score; computing aplurality of candidate mappings between the two TSSTI and a plurality ofmapping scores associated with the candidate mappings; determining aparticular candidate mapping associated with an optimal mapping scoreamong all the candidate mappings as the mapping between the two TSSTI,wherein the optimal mapping score comprises at least one of: a maximumsimilarity score and a minimum mismatch score; and computing arepresentative STI template based on at least one of: the targetsegments of the cleaned training TSSTI, a time derivative of the targetsegments, or a signal processing of the target segments.

Clause 27. The Type2 heterogeneous wireless device of the wirelesstarget motion detection system of clause 24, wherein the set ofinstructions further case the Type2 device to perform: detecting apresence of motion based on at least one of: the TSCI, and the TSSTIbefore detecting the target motion of the object; wherein the wirelesssignal comprises a time series of probing signals withpiecewise-constant instantaneous probe rates corresponding topiecewise-constant instantaneous inter-probe periods; wherein the CI ofthe TSCI have respective piecewise-constant instantaneous CI ratescorresponding to the piecewise-constant instantaneous probe rates;wherein the STI of the TSSTI have respective piecewise-constantinstantaneous STI rates corresponding to the piecewise-constantinstantaneous CI rates of the TSCI; in a standby mode of the onlinestage, configuring the instantaneous STI rates, and the instantaneous CIrates to be a default rate by configuring the instantaneous probe rateto be the default rate; in the standby mode, detecting a presence ofmotion based on at least one of: the CI at the default rate, or the STIat the default rate; in a task mode of the online stage, configuring theinstantaneous STI rates, and the instantaneous CI rates to be anelevated rate by configuring the instantaneous probe rate to be theelevated rate, wherein the elevated rate is greater than the defaultrate; in the task mode, detecting the target motion of the object basedon at least one of: the CI at the elevated rate, or the STI at theelevated rate; and changing between the task mode and the standby mode.

Clause 28. A wireless target motion detection system, comprising: aType1 heterogeneous wireless device in a venue configured to transmit awireless signal through a wireless multipath channel of the venue,wherein an object undergoes a target motion in the venue; and a Type2heterogeneous wireless device in the venue configured to: in an onlinestage, receive the wireless signal through the wireless multipathchannel of the venue, wherein the received wireless signal differs fromthe transmitted wireless signal due to the wireless multipath channel ofthe venue and a modulation of the wireless signal by the objectundergoing the target motion in the venue, obtain a time series ofchannel information (TSCI) of the wireless multipath channel based onthe received wireless signal, compute a time series of spatial-temporalinformation (STI) of the object based on the TSCI, detecting the targetmotion of the object based on at least one of: the time series of STI(TSSTI), or the TSCI, wherein in an offline stage before the onlinestage, at least one representative STI template is computed based on aset of training TSSTI, each training TSSTI computed based on arespective training TSCI obtained based on a respective trainingwireless signal from a respective training Type1 device received by arespective training Type2 device in a respective training venue when arespective training object in the respective training venue undergoes arespective training target motion, wherein each training TSSTI iscleaned by segmenting the training TSSTI into a respective initialnon-target segment of STI, a respective target segment of STIcorresponding to the respective training target motion of the respectivetraining object, and a respective trailing non-target segment of STI,wherein the training TSSTI is segmented based on a mapping between thetraining TSSTI and another training TSSTI, and a mapping score betweenthe two TSSTI associated with the mapping, the mapping score comprisesat least one of: a similarity score and a mismatch score, wherein aplurality of candidate mappings between the two TSSTI are computed, eachcandidate mapping associated with a mapping score; wherein a particularcandidate mapping associated with an optimal mapping score among all thecandidate mappings is determined as the mapping between the two TSSTI,the optimal mapping score comprising at least one of: a maximumsimilarity score and a minimum mismatch score; and wherein arepresentative STI template is computed based on at least one of: thetarget segments of the set of cleaned training TSSTI, a time derivativeof the target segments, or a signal processing of the target segments.

Clause 29. The wireless target motion detection system of clause 28,wherein the Type2 device is further configured to perform: in the onlinestage, detecting a presence of motion based on at least one of: the TSCI, and the TSSTI before detecting the target motion of the object;wherein the wireless signal comprises a time series of probing signalswith piecewise-constant instantaneous probe rates corresponding topiecewise-constant instantaneous inter-probe periods; wherein the CI ofthe TSCI have respective piecewise-constant instantaneous CI ratescorresponding to the piecewise-constant instantaneous probe rates;wherein the STI of the TSSTI have respective piecewise-constantinstantaneous STI rates corresponding to the piecewise-constantinstantaneous CI rates of the TSCI; in a standby mode of the onlinestage, configuring the instantaneous STI rates, and the instantaneous CIrates to be a default rate by configuring the instantaneous probe rateto be the default rate; in the standby mode, detecting a presence ofmotion based on at least one of: the CI at the default rate, or the STIat the default rate; in a task mode of the online stage, configuring theinstantaneous STI rates, and the instantaneous CI rates to be anelevated rate by configuring the instantaneous probe rate to be theelevated rate, wherein the elevated rate is greater than the defaultrate; in the task mode, detecting the target motion of the object basedon at least one of: the CI at the elevated rate, or the STI at theelevated rate; and changing between the task mode and the standby modein the online stage.

Clause 30. A method of a wireless target motion detection system,comprising: in an online stage: transmitting a wireless signal from aType1 heterogeneous wireless device in a venue through a wirelessmultipath channel of the venue, wherein an object in the venue isundergoing a target motion; receiving the wireless signal by a Type2heterogeneous wireless device in the venue through the wirelessmultipath channel, wherein the received wireless signal differs from thetransmitted wireless signal due to the wireless multipath channel of thevenue and a modulation of the wireless signal by the object undergoingthe target motion in the venue; obtaining a time series of channelinformation (TSCI) of the wireless multipath channel based on thereceived wireless signal using a processor, a memory communicativelycoupled with the processor and a set of instructions stored in thememory; computing a time series of spatial-temporal information (STI) ofthe object based on the TSCI; detecting the target motion of the objectbased on a comparison of the time series of STI (TSSTI) with at leastone representation STI template; in an offline stage: computing the atleast one representative STI template based on a set of training TSSTI,each training TSSTI computed based on a respective training TSCIobtained based on a respective training wireless signal from arespective training Type1 device received by a respective training Type2device in a respective training venue when a respective training objectin the respective training venue undergoes a respective training targetmotion; cleaning each training TSSTI by segmenting the training TSSTIinto a respective initial non-target segment of STI, a respective targetsegment of STI corresponding to the respective training target motion ofthe respective training object, and a respective trailing non-targetsegment of STI; segmenting the training TSSTI based on a mapping betweenthe training TSSTI and another training TSSTI with a mapping scorebetween the two TSSTI associated with the mapping, the mapping scorecomprises at least one of: a similarity score and a mismatch score;computing a plurality of candidate mappings between the two TSSTI, eachcandidate mapping associated with a respective mapping score;determining a particular candidate mapping associated with an optimalmapping score among all the candidate mappings as the mapping betweenthe two TSSTI, the optimal mapping score comprising at least one of: amaximum similarity score and a minimum mismatch score; and computing arepresentative STI template is computed based on at least one of: thetarget segments of the set of cleaned training TSSTI, a time derivativeof the target segments, or a signal processing of the target segments;in the online stage: computing a test mapping between the TSSTI and eachrepresentative STI template with respective associated test mappingscore; detecting the target motion of the object if a test mapping scoresatisfies a detection criterion.

The features described above may be implemented advantageously in one ormore computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that may be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., C, Java), including compiled orinterpreted languages, and it may be deployed in any form, including asa stand-alone program or as a module, component, subroutine, abrowser-based web application, or other unit suitable for use in acomputing environment.

Suitable processors for the execution of a program of instructionsinclude, e.g., both general and special purpose microprocessors, digitalsignal processors, and the sole processor or one of multiple processorsor cores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to communicate with, one or more mass storage devices forstoring data files; such devices include magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andoptical disks. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices, such as EPROM,EEPROM, and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory may be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

While the present teaching contains many specific implementationdetails, these should not be construed as limitations on the scope ofthe present teaching or of what may be claimed, but rather asdescriptions of features specific to particular embodiments of thepresent teaching. Certain features that are described in thisspecification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Anycombination of the features and architectures described above isintended to be within the scope of the following claims. Otherembodiments are also within the scope of the following claims. In somecases, the actions recited in the claims may be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

We claim:
 1. A system for target motion detection, comprising: atransmitter configured for transmitting a first wireless signal througha wireless multipath channel of a venue; a receiver configured forreceiving a second wireless signal through the wireless multipathchannel, wherein the second wireless signal differs from the firstwireless signal due to the wireless multipath channel that is impactedby a target motion of an object in the venue; and a processor configuredfor: obtaining a time series of channel information (TSCI) of thewireless multipath channel based on the second wireless signal,computing a time series of spatial-temporal information (STI) of theobject based on the TSCI, and detecting the target motion of the objectbased on the time series of STI (TSSTI).
 2. The system of claim 1,wherein: each STI is related to at least one of following features ofthe object: position change, distance change, speed or acceleration; andthe target motion is a transient motion or a periodic motion.
 3. Thesystem of claim 2, wherein the processor is further configured for:computing a time series of features (TSF) based on the TSCI, wherein theTSSTI is computed based on the TSF, wherein each feature of the TSFcomprises at least one of following characteristics related to acorresponding channel information (CI): magnitude, phase,autocorrelation function (ACF), local maximum, or local minimum.
 4. Thesystem of claim 3, wherein: the object is a human being; the targetmotion is a fall-down motion of the human being; a feature of the TSFcomprises at least one of: a magnitude of a component of a CI, or anautocorrelation function of the TSCI; and each STI comprises the speedor the acceleration.
 5. The system of claim 2, wherein the processor isfurther configured for: computing, during an offline stage of thesystem, at least one representative STI template based on a set oftraining TSSTI, wherein each training TSSTI is computed based on arespective training TSCI obtained based on a respective trainingwireless signal from a respective training transmitter received by arespective training receiver in a respective training venue when arespective training object in the respective training venue undergoes arespective training target motion; and detecting, during an online stageof the system, the target motion of the object based on the TSSTI andthe at least one representative STI template.
 6. The system of claim 5,wherein the processor is further configured for: cleaning the set oftraining TSSTI; and computing the at least one representative STItemplate based on the set of cleaned training TSSTI.
 7. The system ofclaim 6, wherein the processor is further configured for: cleaning eachtraining TSSTI by segmenting the TSSTI into: a respective initialnon-target segment of STI, a respective target segment of STI, and arespective trailing non-target segment of STI, wherein the targetsegment of STI corresponds to the respective training target motion ofthe respective training object.
 8. The system of claim 7, wherein theTSSTI is segmented based on at least one of: a respective firstconstraint on the respective initial non-target segment, a respectivesecond constraint on the respective target segment, or a respectivethird constraint on the respective trailing non-target segment.
 9. Thesystem of claim 8, wherein the processor is further configured for:constraining the respective initial non-target segment to start from abeginning of the TSSTI and to have a first duration that is at least oneof: less than a first threshold, or being a fraction of a duration ofthe TSSTI; constraining the respective target segment to have a secondduration not greater than a target duration associated with the targetmotion of the object; or constraining the respective trailing non-targetsegment to end at an end of the TSSTI and to have a third duration beinga fraction of the duration of the TSSTI.
 10. The system of claim 7,wherein the processor is further configured for: segmenting the trainingTSSTI based on a mapping between the training TSSTI and an additionaltraining TSSTI with a mapping score between the two TSSTI associatedwith the mapping, wherein each STI of training TSSTI is mapped to atleast one STI in the mapping, wherein the mapping score is a similarityscore or a mismatch score.
 11. The system of claim 10, wherein theprocessor is further configured for: computing a plurality of candidatemappings, wherein each candidate mapping is between a candidate targetsegment of the training TSSTI and an additional candidate target segmentof the additional training TSSTI; computing a plurality of mappingscores associated with the candidate mappings, wherein each mappingscore associated with a candidate mapping is normalized based on alength associated with the candidate target segment and the additionalcandidate target segment; and computing the mapping based on theplurality of candidate mappings and the associated mapping scores. 12.The system of claim 11, wherein the processor is further configured for:determining a particular candidate mapping associated with an optimalmapping score among all the candidate mappings, wherein the optimalmapping score comprises at least one of: a maximum similarity score or aminimum mismatch score; choosing the particular candidate mapping as themapping between the two TSSTI; and choosing the associated optimalmapping score as the associated mapping score between the two TSSTI. 13.The system of claim 11, wherein the processor is further configured for:computing iteratively the plurality of mapping scores; determining aseries of partial mappings between subsets of the training TSSTI andsubsets of the additional training TSSTI; and computing iteratively aquantity associated with each partial mapping, wherein each partialmapping is a mapping between a partial segment of the training TSSTI anda partial segment of the additional training TSSTI, wherein the seriesof partial mappings comprises the plurality of candidate mappings. 14.The system of claim 13, wherein the processor is further configured for:in an initial iteration, initializing iteration by determining aninitial partial mapping between a single STI of the training TSSTI and asingle STI of the additional training TSSTI and computing an initialquantity associated with the initial partial mapping; in subsequentiterations, computing iteratively the quantity associated with eachpartial mapping based on a value associated with a partial mappingcomputed in a previous iteration, wherein the partial mapping is betweena first segment of the training TSSTI and a second segment of theadditional training TSSTI, and the additional partial mapping is betweena third segment of the training TSSTI and a fourth segment of theadditional TSSTI, wherein the third segment is a subset of the firstsegment, wherein the fourth segment is a subset of the second segment;and computing the plurality of mapping scores based on the quantitiesassociated with the series of partial mappings.
 15. The system of claim13, wherein the processor is further configured for: in an initialiteration, initializing iteration by determining a group of initialpartial mappings comprising a mapping between a single STI of thetraining TSSTI and a single STI of the additional training TSSTI, andcomputing initial quantities associated with the group of initialpartial mappings; in subsequent iterations, computing iterativelyquantities associated with a group of partial mappings based on at leastone value computed in at least one previous iteration; and computing theplurality of mapping scores based on the quantities associated with theseries of partial mappings.
 16. The system of claim 14, wherein: thequantity associated with a partial mapping is a function of a measurecomponent associated with the partial mapping and a length componentassociated with the partial mapping; the measure component is asimilarity component or a mismatch component; and the processor isfurther configured for: in the initial iteration, computing an initialmeasure component and an initial length component associated with theinitial partial mapping, and in subsequent iterations, computingiteratively a measure component and a length component associated witheach partial mapping based on at least one of: a measure componentcomputed in a past iteration or a length component computed in anadditional past iteration, and computing the quantity based on thefunction.
 17. The system of claim 10, wherein the processor is furtherconfigured for: segmenting the training TSSTI based on a plurality ofpairwise mappings, wherein each pairwise mapping is a mapping betweenthe training TSSTI and one of the remaining training TSSTI; computing aplurality of candidate segmentations of the training TSSTI each based ona respective pairwise mapping, each candidate segmentation comprising acandidate initial non-target segment, a candidate target segment, and acandidate trailing non-target segment of the training TSSTI; andsegmenting the training TSSTI by combining the plurality of candidatesegmentations.
 18. The system of claim 17, wherein the processor isfurther configured for: determining two respective candidate boundarypoints of each candidate segmentation, wherein the two respectivecandidate boundary points includes: a respective candidate firstboundary point between the respective candidate initial segment and therespective candidate target segment, and a respective candidate secondboundary point between the respective candidate target segment and therespective candidate trailing segment of the candidate segmentation;segmenting the training TSSTI by computing a first boundary pointbetween the initial segment and the target segment of the training TSSTIas a first function of the candidate first boundary points of theplurality of candidate segmentation; and segmenting the training TSSTIby computing a second boundary point between the target segment and thetrailing segment of the training TSSTI as a second function of thecandidate second boundary points of the plurality of candidatesegmentation.
 19. The system of claim 7, wherein the processor isfurther configured for: computing a representative STI template based onat least one of: the target segments of the cleaned training TSSTI, atime derivative of the target segments, or a signal processing of thetarget segments.
 20. The system of claim 19, wherein the representativeSTI template is computed by: computing an optimal TSSTI based on anoptimization of a combined score with respect to the set of cleanedtraining TSSTI, wherein the optimization comprises at least one of:maximization, minimization, constrained maximization, or constrainedminimization, wherein the combined score comprises an aggregation of atleast one of: a plurality of first scores each being a first pairwisescore between the TSSTI and a cleaned training TSSTI based on a pairwisemapping between the TSSTI and the cleaned training TSSTI, a plurality ofsecond scores each being a second pairwise score between the timederivative of the TSSTI and the time derivative of a cleaned trainingTSSTI based on the pairwise mapping, or a plurality of third scores eachbeing a third pairwise score between the signal processed TSSTI and asignal processed cleaned training TSSTI based on the pairwise mapping,wherein the aggregation comprises at least one of: sum, mean, ordiscriminative cost, wherein the discriminative cost comprises aweighted difference between (a) a first normalized aggregation of afirst pairwise mapping score between the TSSTI and each of a firstsubset of the cleaned training TSSTI, and (b) a second normalizedaggregation of a second pairwise mapping score between the TSSTI andeach of a second subset of the cleaned training TSSTI, wherein anynormalized aggregation comprises at least one of: mean or weighted mean.21. The system of claim 5, wherein the processor is further configuredfor: in the offline stage, computing the at least one representative STItemplate based on a first dynamic time warping (DTW); and in the onlinestage: computing a test mapping between the TSSTI and eachrepresentative STI template with a respective associated test mappingscore based on a second DTW, and detecting the target motion of theobject when any test mapping score satisfies a detection criterion. 22.The system of claim 2, wherein the processor is further configured for:before detecting the target motion of the object, detecting a presenceof motion of the object based on the TSCI with a first probing rate,wherein the target motion of the object is detected based on a secondprobing rate higher than the first probing rate.
 23. The system of claim22, wherein: the first wireless signal comprises a time series ofprobing signals with piecewise-constant instantaneous probing ratescorresponding to piecewise-constant instantaneous inter-probe periods;each CI of the TSCI has a respective piecewise-constant instantaneous CIrate corresponding to a piecewise-constant instantaneous probing rate;each STI of the TSSTI has a respective piecewise-constant instantaneousSTI rate corresponding to a piecewise-constant instantaneous CI rate ofthe TSCI; and the processor is further configured for in a standby modeof the system, configuring the instantaneous STI rates and theinstantaneous CI rates to be a default rate by configuring theinstantaneous probing rate to be the default rate, in the standby mode,detecting the presence of the motion based on at least one of: the CI atthe default rate, or the STI at the default rate, in a task mode of thesystem, configuring the instantaneous STI rates and the instantaneous CIrates to be an elevated rate by configuring the instantaneous probingrate to be the elevated rate, wherein the elevated rate is greater thanthe default rate, in the task mode, detecting the target motion of theobject based on at least one of: the CI at the elevated rate, or the STIat the elevated rate, and switching between the task mode and thestandby mode.
 24. A wireless device of a wireless target motiondetection system, comprising: a processor; a memory communicativelycoupled to the processor; and a receiver communicatively coupled to theprocessor, wherein: an additional wireless device of the wireless targetmotion detection system is configured for transmitting a first wirelesssignal through a wireless multipath channel of a venue to the receiver,the receiver is configured for receiving a second wireless signalthrough the wireless multipath channel, the second wireless signaldiffers from the first wireless signal due to the wireless multipathchannel that is impacted by a target motion of an object in the venue,and the processor is configured for: obtaining a time series of channelinformation (TSCI) of the wireless multipath channel based on the secondwireless signal, computing a time series of spatial-temporal information(TSSTI) of the object based on the TSCI, and detecting the target motionof the object based on at least one of: the TSSTI or the TSCI.
 25. Thewireless device of claim 24, wherein the processor is further configuredfor: in an offline stage, computing at least one representative STItemplate based on a set of training TSSTI, wherein each training TSSTIis computed based on a respective training TSCI obtained based on arespective training wireless signal from a respective training wirelessdevice received by a respective training wireless device in a respectivetraining venue when a respective training object in the respectivetraining venue undergoes a respective training target motion; and in anonline stage, detecting the target motion of the object based on theTSSTI and the at least one representative STI template.
 26. The wirelessdevice of claim 25, wherein the processor is further configured for:cleaning each training TSSTI by segmenting the TSSTI into: a respectiveinitial non-target segment of spatial-temporal information (STI), arespective target segment of STI, and a respective trailing non-targetsegment of STI, wherein the target segment of STI corresponds to therespective training target motion of the respective training object;segmenting the training TSSTI based on a mapping between the trainingTSSTI and an additional training TSSTI, and based on a mapping scorebetween the two TSSTI associated with the mapping, wherein each STI ofthe training TSSTI is mapped to at least one STI in the mapping, whereinthe mapping score is a similarity score or a mismatch score; computing aplurality of candidate mappings between the two TSSTI; computing aplurality of mapping scores associated with the candidate mappings;determining a particular candidate mapping associated with an optimalmapping score among all the candidate mappings, as the mapping betweenthe two TSSTI, wherein the optimal mapping score comprises at least oneof: a maximum similarity score or a minimum mismatch score; andcomputing a representative STI template based on at least one of: thetarget segments of the cleaned training TSSTI, a time derivative of thetarget segments, or a signal processing of the target segments.
 27. Thewireless device of claim 25, wherein: the first wireless signalcomprises a time series of probing signals with piecewise-constantinstantaneous probing rates corresponding to piecewise-constantinstantaneous inter-probe periods; each CI of the TSCI has a respectivepiecewise-constant instantaneous CI rate corresponding to apiecewise-constant instantaneous probing rate; each STI of the TSSTI hasa respective piecewise-constant instantaneous STI rate corresponding toa piecewise-constant instantaneous CI rate of the TSCI; and theprocessor is further configured for in a standby mode of the onlinestage, configuring the instantaneous STI rates and the instantaneous CIrates to be a default rate by configuring the instantaneous probing rateto be the default rate, in the standby mode, detecting a presence ofmotion of the object based on at least one of: the CI at the defaultrate, or the STI at the default rate, switching from the standby mode toa task mode of the online stage, in the task mode, configuring theinstantaneous STI rates and the instantaneous CI rates to be an elevatedrate by configuring the instantaneous probing rate to be the elevatedrate, wherein the elevated rate is greater than the default rate, and inthe task mode, detecting the target motion of the object based on atleast one of: the CI at the elevated rate, or the STI at the elevatedrate.
 28. A method of a target motion detection system, comprising:transmitting, by a transmitter, a first wireless signal through awireless multipath channel of a venue to a receiver; receiving, by thereceiver in an online stage of the target motion detection system, asecond wireless signal through the wireless multipath channel, whereinthe second wireless signal differs from the first wireless signal due tothe wireless multipath channel that is impacted by a target motion of anobject in the venue; obtaining a time series of channel information(TSCI) of the wireless multipath channel based on the second wirelesssignal, using a processor, a memory communicatively coupled with theprocessor and a set of instructions stored in the memory; computing atime series of spatial-temporal information (TSSTI) of the object basedon the TSCI; and detecting the target motion of the object based on atleast one of: the TSSTI or the TSCI.
 29. The method of claim 28, furthercomprising: in an offline stage before the online stage, computing atleast one representative STI template based on a set of training TSSTI,wherein each training TSSTI is computed based on a respective trainingTSCI obtained based on a respective training wireless signal from arespective training transmitter received by a respective trainingreceiver in a respective training venue when a respective trainingobject in the respective training venue undergoes a respective trainingtarget motion; cleaning each training TSSTI by segmenting the trainingTSSTI into: a respective initial non-target segment of STI, a respectivetarget segment of STI corresponding to the respective training targetmotion of the respective training object, and a respective trailingnon-target segment of STI, wherein the training TSSTI is segmented basedon a mapping between the training TSSTI and an additional trainingTSSTI, and based on a mapping score between the two TSSTI associatedwith the mapping, wherein the mapping score comprises at least one of: asimilarity score or a mismatch score, computing a plurality of candidatemappings between the two TSSTI; computing a plurality of mapping scoresassociated with the candidate mappings; determining a particularcandidate mapping associated with an optimal mapping score among all thecandidate mappings, as the mapping between the two TSSTI, wherein theoptimal mapping score comprises at least one of: a maximum similarityscore or a minimum mismatch score; and computing a representative STItemplate based on at least one of: the target segments of the cleanedtraining TSSTI, a time derivative of the target segments, or a signalprocessing of the target segments.
 30. The method of claim 29, wherein:the first wireless signal comprises a time series of probing signalswith piecewise-constant instantaneous probing rates corresponding topiecewise-constant instantaneous inter-probe periods; each CI of theTSCI has a respective piecewise-constant instantaneous CI ratecorresponding to a piecewise-constant instantaneous probing rate; eachSTI of the TSSTI has a respective piecewise-constant instantaneous STIrate corresponding to a piecewise-constant instantaneous CI rate of theTSCI; and the method further comprises: in a standby mode of the onlinestage, configuring the instantaneous STI rates and the instantaneous CIrates to be a default rate by configuring the instantaneous probing rateto be the default rate, in the standby mode, detecting a presence ofmotion of the object based on at least one of: the CI at the defaultrate, or the STI at the default rate, switching from the standby mode toa task mode of the online stage, in the task mode, configuring theinstantaneous STI rates and the instantaneous CI rates to be an elevatedrate by configuring the instantaneous probing rate to be the elevatedrate, wherein the elevated rate is greater than the default rate, and inthe task mode, detecting the target motion of the object based on atleast one of: the CI at the elevated rate, or the STI at the elevatedrate.